Performance improvement of mimo mc cdma system using equalization, beamforming and relay
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This thesis examines performance improvement techniques for MIMO MC CDMA systems using equalization, beamforming, and relaying. It proposes using minimum mean square error (MMSE) equalization at the receiver to mitigate inter-symbol interference caused by multipath fading. It also incorporates a modified pilot channel estimation and improved transmit beamforming to strengthen the equalization. Finally, it utilizes novel relays to further extend coverage areas with high traffic density. Simulation results show that the proposed techniques enhance system performance by reducing inter-symbol interference and increasing capacity and quality of transmission.
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N0 Single sided power spectral density
Nb Number of symbols in nth
Subcarrier
Nc Subcarrier
Nd Destination or Receiving antenna
Nr Relay antenna
Ns Source ortransmitting antenna
P(n)
Precoding matrix for source signal
P{.} Probability
r Received vector
r(t) Received signal at time t
Rl lth
element of the received vector r
S l lth
element of the vector S
S(n)
(t) Signal vector at time t
S(t) Transmitter signal at time t
T
Transpose
T Bit duration
Ts Symbol duration
uTs(t) Rectangular waveform with unit amplitude
X(k) Transmitted data samples
x(n)
(t) Precoded signal vector at time t
x(t) Channel precoding matrix for source
xr(t2) Amplified signal of relay at t2
Y(k) Received signal
yd(t) Received signal at the destination at time t
YP(k) Received pilot signals
yr(t) Received signal at the relay at time t
(.)* Complex conjugation
(n)
nth
Subcarrier
||.|| Norm of a vector
∆2
(.,.) Squared Euclidean distance
αn Equalizer co efficient
β Equalization parameter with value in the range of [-1,1]](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-22-320.jpg)
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amount of dispersion in time caused by multipath delay spread decreases. Although
OFDM is robust to frequency selective fading, it has severe disadvantages in SC
synchronization and sensitivity to frequency offset estimation. The other main issue
of OFDM is the presence of a large number of SCs which exhibits a non constant
nature in its envelope.
Future generation is to cater more users with enriched services, CDMA has a
strong candidate to support multimedia mobile services as opposed in conventional
schemes such as TDMA/FDMA which also has the ability to cope up with the
asynchronous nature of the multimedia traffic. By employing Rake receivers CDMA
systems can coherently combine the multipath components due to the hostile frequency
selective channel and the processing gain due to spreading provides robustness to the
multi user interference. The use of conventional CDMA does not seem to be realistic
when the data rates go up to a hundred megabits per second due to severe ISI(inter
symbol interference) and the difficulty in synchronizing a fast sequence.
Further to enhance the performance, the MIMO antennas at both ends of a
wireless link helps to realize high data rate through spatial multiplexing and
drastically improve the spectral efficiency of the system and provide link reliability
in rich scattering environments through spatial diversity. In addition, the capacity of
MIMO system increases linearly with the number of transmit receive antenna pairs
without increasing transmitted power. Even though multicarrier modulation and
MIMO mitigate ISI, effectiveness of this suppression is limited and both of these two
techniques cannot work perfectly in fast fading environment [1-3]. To overcome the
problem, this work proposes equalization and channel estimation in the receiver BF in
the transmitter and relay in the network to reduce the ISI, cater more number of users
and improve the performance.
1.2.1 CDMA
In CDMA the information signals of different users are modulated by
orthogonal or non orthogonal spreading codes. The resulting spread signals
simultaneously occupy the same time and bandwidth, as shown in Fig.1.1. The](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-26-320.jpg)
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receiver uses the spreading code structure to separate out the different users. This
method has the feature that the unwanted signals with different code will get spread
even more by the process and appear like noise to the receiver. Some of the
properties that have made CDMA useful are: signal hiding, non interference with
existing systems, anti jam, interference rejection, information security and multipath
tolerance. Though CDMA ensure effective utilization of bandwidth, it is not capable
of transmitting data at rates of around 5 to 10 Mbps. Various studies indicate that
some sort of multi carrier modulation (MCM) scheme can only support higher data
rates and be suitable for mobile paths.
Fig.1.1 Code division multiple access
1.2.2 OFDM
OFDM is the basic MCM scheme that has been extremely used for high data
rate application. In OFDM, the available spectrum is divided into many carriers,
each one being modulated by a low rate data stream. Like FDMA, in OFDM also
[2-4] the multiple user access is achieved by subdividing the available bandwidth
into multiple narrow band channels, which are allocated to users. However, OFDM
uses the spectrum much more efficiently by spacing the channels much closer
together with no overhead as in FDMA. This is achieved by making all the carriers
orthogonal to one another, thus preventing interference between the closely spaced
carriers as shown in Fig. 1.2.
Time
Frequency](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-27-320.jpg)
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Fig.1.2 Comparison of the bandwidth utilization for FDM and OFDM
Each carrier in an OFDM signal has a very narrow bandwidth
(i.e. 1 kHz), thus the resulting symbol rate is low. This result in the signal having a
high tolerance to multipath delay spread, as the delay spread must be long enough to
cause significant inter symbol interference (e.g. > 100 ms). Fig.1.3 and 1.4 show a
simple OFDM transmitter and OFDM receiver respectively.
OFDM has a serious limitation of being sensitive to nonlinear amplification,
frequency and phase errors in SCs. Moreover the mobile community may not be
interested in loosing the merits of CDMA and therefore possibilities of mixing
CDMA and MCM have been initiated. Researchers have analyzed three different
combinations of OFDM and CDMA; multi carrier (MC) CDMA, MC direct
sequence (DS) CDMA and multi tone (MT) CDMA [5], out of which MC CDMA is
chosen as possible access scheme for beyond 3G mobile communication systems.
Frequency
Conventional Frequency Division Multiplex (FDM)
Frequency
Saving of bandwidth
Orthogonal Frequency Division Multiplex (OFDM)](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-28-320.jpg)
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1.2.3 Multicarrier CDMA
The MC CDMA transmitter spreads the original data stream in the frequency
domain over different SCs [6] using a given spreading code as depicted in Fig. 1.5.
In this system the SCs convey the same information at one time. The binary data dk
are first multiplied by the spreading code (PN binary sequences) ck(i) and the bit
duration T of the data is much longer than the PN sequence chip duration Tc.
Fig.1.5 A simple MC CDMA transmitter
Normally N=T/TC may be equal to 32, 64, 128 or higher. After the necessary
encoding, the sequence that corresponds to one data bit is modulated in the band of
N modulators. A very efficient way of realizing such a modulation scheme is
through the inverse fast fourier transform (IFFT). The distance between SC is equal
to the chip rate or its multiple. The guard time/ cyclic prefix should be introduced
for each symbol in order to combat the inter symbol interference caused by the
channel and filters in the system. At the output of IFFT the pulse shaping filter
followed by a modulator shifts the whole spectrum to the desirable frequency band.
Another solution is to apply the offset IFFT that eliminates the necessity of the
above mentioned frequency shift. In the MC CDMA system, SC frequencies are
usually chosen to be orthogonal to each other, i.e., SC frequencies attempt to satisfy
the following condition:
∑dk (t)
ck (1)
ck (N)
exp (j 2πf1t)
exp (j 2πfNt)
sk (t)](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-30-320.jpg)
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The use of multiple antennas in the receiver and/or in the transmitter
basically provides the following advantages [7].
Array gain: Array gain is an increase in average received SNR obtained
by coherently combined signals transmitted from multiple antennas or
received by multiple antennas. In order to get array gain channel
knowledge at the transmitter or receiver is required.
Diversity gain: Diversity gain refers to improved link reliability in
space, time, frequency or polarization. Diversity gain can be exploited in
the case of independently fading multiple channels experienced by
different antennas in order to mitigate the effect of multipath fading
Spatial multiplexing gain: Multiplexing gain corresponds to the
increased data rate due to the ability to transmit multiple parallel data
streams over MIMO channel without increasing the bandwidth or total
transmit power. The capability to support multiple streams depends on
the rank of the channel matrix H.
As a result, the MIMO technology can be used to achieve improved system
performance, such as increased spectral efficiency, increased user throughput and
better coverage.
1.3 LITERATURE SURVEY
Today‟s wireless communication requires high data rate and good quality,
which is possible only if the received signal is not affected by channel impairments.
The diversity combining schemes are applied to combine the multipath received
signals of a diversity reception device into a single improved signal. These schemes
are applied at the receiver to mitigate the effect of channel fading on the received
signal to obtain the signal with maximum signal to noise ratio (SNR).](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-32-320.jpg)
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Various types of combating techniques for channel impairments are proposed
in the literature based on noise, interference and capacity for OFDM, MC CDMA
and MIMO system. The signals of different SCs are appropriately weighted and
summed using EGC [8-11], MRC [12-15], ORC or zero forcing (ZF) [16-18] and
threshold based ORC (TORC) [19]. The MRC technique represents the optimal
choice when the system is noise limited; in contrast, when the system is interference
limited, the ORC can completely eliminate MAI in the downlink transmission; yet, it
causes noise amplification in the receiver [20]. The MMSE [21-26] criterion may
also be used to derive the equalizer coefficients, whereas an even more powerful
optimization criterion is the minimum bit error ratio (MBER) criterion [27].
A lot of research studies have been dedicated to the error performance
evaluation of MC CDMA systems [28-31], and the approximation of the bit error
rate (BER) for MC CDMA with MRC, EGC, and MMSEC has been derived [28].
However, it is based on the law of large numbers, i.e. the spreading code length must
be sufficiently large. In [29], the authors have derived the exact BER of a
synchronous MC CDMA system with MRC. The BER is calculated based on a
moment generating function method with no assumption on the distribution of
multiple access interference. The BER performance of MC CDMA with MRC and
EGC has been evaluated over a Rayleigh fading channel with correlated envelopes
and phases [30]. A lower bound and a tight approximation on the BER of MC
CDMA with ORC is presented in [31].Although MRC, EGC and ORC only requires
the channel state information (CSI), the MMSE and MBER equalizers are more
complex because they exploit additional knowledge of SNR.
Partial equalization (PE) [32-34] is another technique to reduce the ISI,
where the signals are partially equalized to avoid noise and interference in the
receiver instead of complete equalization. A different approach has been proposed in
[35,36] in the form of combined equalization. This technique applies pre
equalization at the transmitter in conjunction with post equalization at the receiver
which requires channel knowledge at both the transmitter and receiver ends
increasing the complexity of the system. In [37] combined partial equalization was
discussed where partial equalization was considered at the transmitter and TORC at](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-33-320.jpg)
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the receiver. From the literature survey it is observed that MMSE equalization is
good choice inspite of its complexity. Many researchers concentrate only on
equalization but not on channel estimation. CSI is a prime factor for equalization to
reduce the amplitude and phase distortion.
Differential demodulation without channel estimation was used in [1] which
results in SNR loss of upto 3 dB. The channel tracking method using kalman filter
has been analyzed which results in better estimation accuracy but suffers from
computational complexity and delay [38, 39]. Various authors have reported the
effect of channel estimation error and phase noise [40-43]. Some authors have tried
channel estimation using known data at the receiver. It involves sending pilots
(symbols which are known to the receiver) with the data symbols so that the channel
can be estimated and therefore the data at the receiver.
Training sequence and optimal training based channel estimation is discussed
for MIMO system [44,45] in which with increasing number of transmitting and
receiving antennas, the length of the training sequence needs to be increased, leaving
less space for data transmission and reduced overall spectral efficiency. However the
optimal choice of training sequence is quite challenging. In [46, 47] the channel
estimation is totally avoided by introducing differential modulation at the expense of
a performance penalty, however the spectral efficiency of MIMO system depends
upon the channel estimation.
Most channel estimation methods for OFDM transmission systems have
been developed under the assumption of a slow fading channel, where the channel
transfer function is assumed stationary within one OFDM data block. In addition the
channel transfer function for the previous OFDM data block is used as the transfer
function for the present data block. In practice, the channel transfer function of a
wide band radio channel may have significant changes even within one OFDM data
block. Therefore, it is preferable to estimate channel characteristics based on the
pilot signal in each individual OFDM data block [48-50].](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-34-320.jpg)
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To increase the spectral efficiency less number of pilots operating in time
varying channels [51], dynamic pilot symbol arrangement with wiener filter [52] is
proposed. Channel estimation technique for MIMO OFDM is studied [53, 54],
concentrating only on OFDM but not on MIMO system. To avoid increasing the
length of pilot, optimal/training based channel estimation is discussed for MIMO
OFDM system [55-58], and MIMO CDMA [59] but real challenge is in arriving the
optimal number of pilots Incorporating pilots decreases the spectral efficiency of the
system and they usually require averaging over many symbols before obtaining a
good estimate which make the scheme inefficient for high speed data
communication.
The limitations in training based estimation techniques motivated interest in
the spectrally efficient blind approach [60-71]. They utilize certain underlying
mathematical information about the kind of data being transmitted. Existing blind
methods can be broadly categorized as statistical or deterministic: The former
methods rely on assumptions on the statistics of the input sequence [60], while the
latter make no such assumption [61]. In the first category, i.e., statistical approach,
blind channel estimation using second order statistics can potentially achieve
superior performance for a given time averaging interval than approaches based on
higher order statistics [62]. The second category, i.e., deterministic method, is
generally favored when the input statistics are unknown, or there may not be
sufficient time samples to estimate them. Several interesting deterministic methods
were discussed in [63]; however most of them are exclusively for SISO or single
carrier transmissions.
Amid second order statistics based blind approaches, subspace based
estimation are attractive since estimates can often be obtained in a simple form by
optimizing a quadratic cost function [64]. Without employing any precoder, a
subspace based method is proposed for OFDM systems by utilizing the redundancy
introduced by the cyclic prefix (CP) [65], and it is further extended for MIMO
OFDM systems in [66]. Virtual carriers (VCs) are SCs that are set to zero with no
information being transmitted, which can be used for channel estimation. Such a](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-35-320.jpg)
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scheme is proposed for OFDM systems [67] and further extended to MIMO OFDM
systems in [68].
Variants of the statistics based methods for example by inserting zero
padding instead of CP for each OFDM block or by introducing the so called
repetition index and remodulation on the received signal [69-71]. However, the
number of required time samples is still implicitly proportional to the size of the
IFFT in the OFDM modulator. It is also noted that deterministic approaches still
need to accumulate data samples to algebraically obtain channel estimates, and their
performance in noise improves as the number of samples increases. Therefore, as the
dimension of the parameter space is increased in the MIMO OFDM context, the
number of samples required for deterministic methods to achieve an acceptable level
of performance will also inevitably be increased. Eventhough these methods are
bandwidth efficient but are notoriously slow to converge, extremely computationally
intensive and hence are impractical to implement in real time systems.
Channel estimation based on semi blind techniques are proposed in order to
combine the advantage of pilot based channel estimation and semi blind approach
[72-76]. These methods use pilots to obtain initial CSI and blind approach methods
are used to improve the system performance. However obtaining optimal value of
pilot signals are difficult. In [73] authors develop a semi blind channel estimation
method for MIMO OFDM systems based on a specific and non redundant precoding
scheme, say, circular precoding, since the circular precoding allows channel
estimation at the receiver and simplifies the encoding scheme at the transmitter. In
[76], an efficient semi blind sparse channel estimation approach for MIMO OFDM
systems was proposed. Through the analysis of the second order statistics of the
received signal that passes through a sparse channel, a blind algorithm for obtaining
a constraint on the sparse channel vector with respect to the MSTs is developed. By
combining this blind constraint with a training based sparse LS criterion, a semi
blind solution to the estimation of the effective channel is then obtained.](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-36-320.jpg)
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It is possible to use the CSI for BF also, a representative MIMO technique
which is a promising solution that could be utilized to enhance throughput [77-80].
There are many available beam design algorithms for MIMO interference channels
such as interference alignment based algorithms [81-83] and sum rate targeted
algorithms [81-86]. However, most of these algorithms assume perfect CSI at
transmitters and receivers, whereas the assumption of perfect CSI is unrealistic in
practical wireless communication systems since perfect CSI is hard to obtain
practically due to channel estimation error, limited feedback or other limitations
[87]. Thus, the CSI error should be incorporated into the beam design to yield better
performance, and this is typically done under robust beam design frameworks.
One approach for multi antenna channels [88-90] is based on quantization of
the instantaneous CSI at the receiver followed by its conveyance to the transmitter
using a low rate feedback channel. This is a limited feedback approach, there are
also other approaches [91-93] which are based on feedback of partial (or statistical)
channel information. These methods, in general, do not perform well as the ones
using instantaneous feedback since they do not track the rapid fluctuations of the
channel. There are more general feedback schemes [94-96] that dynamically adapt
to the distribution of the channel, but these methods are generally too complicated to
implement in practice. In [95-97] and [98-104], the MIMO BF vector is quantized at
the receiver using a fixed codebook available at both the transmitter and the
receiver. There are two challenges in designing a MIMO BF system using quantized
feedback. One is how to design a good codebook, and the other is to decide on a BF
weight vector in a codebook efficiently. The unitary DFT matrix codebook and the
Grassmannian codebook were proposed in [97] and [89], respectively. In [101], an
iterative search algorithm that has linear complexity on average with respect to the
number of transmit antennas was proposed. Random BF(RBF) has been widely
studied due to its simplicity and lower feedback overhead, although its performance
is poorer than that of other orthogonal BF schemes that utilize full channel
information [105-108]. Since RBF uses only SINR feedback rather than the full
channel information, the feedback overhead can be efficiently reduced.](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-37-320.jpg)
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In MIMO OFDM, BF is performed independently on each SCs, which is
referred to as subcarrier wise beamforming [109,110], to exploit the spatial
diversity. Unfortunately, subcarrier wise beamforming induces high computational
complexity since a discrete fourier transform (DFT) operation is required for each
antenna element [111,112]. In addition, the optimized transmit weight vector for
every SC has to be fed back to the transmitter, which leads to the growth of feedback
overhead linearly in proportion to the number of SC. To reduce the overall
complexity and the feedback overhead, several schemes have been proposed for
OFDM systems with single or multiple antennas in the literature [111-115] and can
be classified as pre DFT beamforming or symbol wise beamforming which increase
the complexity of implementation because of BF operation in both transmitter and
receiver. The distributed BF schemes presented in [116-118] assume that the
transmitter, the receiver and the relay nodes all use a single antenna. As a result,
these schemes do not benefit from spatial processing at the nodes.
Relays can be deployed in wireless networks to extend coverage and improve
throughput[119]. Relays can be either full duplex or half duplex. Full duplex relays
can transmit and receive at the same time while half duplex relays cannot. Because
full duplex relays are difficult to implement, practical systems focus on half duplex
relays [120,121]. Relays are also classified based on how they process the received
signal. The most popular relay types are decode and forward (DF) [122-124] and
amplify and forward (AF) [125-127]. DF relays have higher computational
complexity due to the requirement of decoding the signals and are helpful only if
they can decode successfully the signals. Transparent to the modulation and coding
of the signals, AF relays can be used flexibly in heterogeneous networks comprising
of many nodes of different complexity or standards [114]. AF relays provide better
diversity performance and in some cases higher throughput (for example, when the
source relay channel is weak) than DF relays in single antenna half duplex relay
systems [128,129]. AF relays may be attractive in practice due their lower
complexity and faster signal processing.
MIMO techniques can be incorporated into relay systems by equipping each
node with multiple antennas to provide multiplexing and diversity gains [130].](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-38-320.jpg)
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Transceiver designs for traditional one way MIMO relay systems were proposed in
[131-135,155]. The optimal two way relay strategies were studied in [136,157] to
maximize achievable sum rate and to achieve optimal diversity multiplexing
tradeoff. For an analog network coding (ANC) based MIMO two way relay system
consisting of a multi antenna relay node and two single antenna source nodes, the
optimal relay BF was studied in [137,156] to characterize the achievable capacity
region, and also to minimize the mean squared error [138]. In [139] and [140], the
authors considered jointly optimizing the source and relay pre coders to maximize
sum rate for MIMO two way relay systems where all nodes are equipped with
multiple antennas. The joint optimization problem was also investigated in [141]
based on the MMSE criterion, and an iterative algorithm as well as a heuristic
algorithm based on channel parallelization was proposed therein. In [142] and [143],
the authors addressed joint source and relay optimization for multiple relay systems.
All of the above mentioned works assume perfect CSI is available. In practice, CSI
has to be estimated using pilot symbols, and channel estimation errors are inevitable
due to noise and the time varying nature of wireless channels. Hence, it is desirable
to provide robustness against channel uncertainties when designing transceiver.
Further Capacity bounds for MIMO relay channels are studied in the
literature [144,136]. Automatic repeat request techniques for MIMO relays are
proposed to achieve the optimal diversity multiplexing delay tradeoff [145] and to
improve throughput and outage probability performances [146]. MIMO relay with
multiuser linear precoding schemes in fixed relay networks have been considered in
[147,148] where CSI is assumed to be known at the transmitter. Relay gain matrix
optimization was studied in [131,132] to maximize the source destination capacity
and linear transceiver designs were proposed based on the MMSE criterion [135].
Full duplex relay discussed for MC CDMA system [149,150] is difficult to
implement due to the generation of self interference and different types of relaying
technique for MIMO OFDMA system is discussed in [151-154]. However, most of
MIMO relay techniques require high complex equalizers and also error free
forwarding at the relay to enjoy the diversity, which is impractical and thus may not
be suitable for low complexity communication environments.](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-39-320.jpg)
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The use of OFDM and cyclic prefix mitigates the effect of time dispersion.
However, it is still necessary to remove the amplitude and phase shift caused by
multipath effect on the channel. The effect of ISI can be counteracted by different
measures such as time or frequency domain equalization. The function of equalizer
within a receiver is to compensate for the average range of expected channel
amplitude and delay variations, also it may be adaptive as the channel is generally
unknown and time varying.
2.2 DIFFERENT TYPES OF EQUALIZATION TECHNIQUE
Many different detection schemes have been proposed in the literature for
OFDM and MC CDMA signal detection [158,159,179]. Every detection technique is
characterized by its specific combining parameters. The basic detection technique
treat the signals from other users as interference and their combining parameters are
functions of multipath fading channel.
2.2.1 Equal Gain Combining (EGC)
The EGC weights equally each sub channel contribution and compensate
only the phases. In this case SCs are processed with the same amplification and
correlation is performed by the means of Walsh matrix. The EGC method would be
a suitable approach for an additive white Gaussian noise (AWGN) channel where all
transfer factors are equal but in a fading channel the transfer factors of the SCs have
different amplitudes [160-162]. For this reason the orthogonality of the Hadamard
codes is violated and interference between the information symbols in the received
signal creates large error floor.
2.2.2 Maximum Ratio Combining (MRC)
In this method the signals from all the SCs are weighted according to their
individual signal power to noise power ratios and then summed. The individual
signal should be cophased before being summed which generally requires individual
receiver and phasing circuit [163-170]. MRC produces an output SNR equal to sum
of the individual SNRs. Thus it has an advantage of producing an output with an](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-43-320.jpg)
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acceptable SNR even when none of the individual signals are themselves acceptable.
But this method performs well only when the signals are quite above noise level. In
this technique, if fading occurs in any one of the branches, a maximum gain is
applied to it which enhances the noise level and totally induce non orthogonality
between the codes.
2.2.3 Orthogonality Restoring Combining (ORC)
While EGC may be desirable for simplicity and MRC for combating noise,
neither of these techniques significantly exploit the coding of the SC. Good choice
is to restore the orthogonality between the sequence through ORC, which implies a
total cancellation of multiuser interference; but this method enhances the noise
because the sub channels with low SNR have higher weights as shown in
Fig.2.2.[171-173].
Fig.2.2 Noise Enhancement in ORC
2.2.4 Threshold Orc (TORC)
With TORC (or Controlled Equalization), an attempt at restoring the
orthogonality between users is made by normalizing the amplitudes of the SCs. As
the orthogonality of the users are encoded in the phase of the SCs, it is primarily
beneficial in the downlink where phase distortion for all users may be corrected and
in this, SCs above a certain threshold will be equalized and retained. This
constraint is added to prevent the amplification of SCs with small amplitudes that
may be dominated by a noise component. A threshold for amplitude of the transfer
factor is introduced to suppress the weak SCs. The bit error rate depends on the
Noise Enhancement
f
Channel Noise
f
Equalizer
f
Information
f
Information
and Noise
f](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-44-320.jpg)
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noise level and on interference due to SCs below the threshold. Both effects show a
different behavior. Interference depends on the probability of rejecting a SC and
dearly rises with threshold. The interference is especially responsible for the error
floor at high SNR. The noise level is reduced with rising threshold because fewer
weak SCs are accepted. At moderate and low thresholds the average noise level is
increased hence it is difficult to determine the optimum threshold in fast fading.
2.2.5 Partial Equalization (PE)
An equalizer with partial channel amplitude compensation is similar in
structure to a ORC. The only difference is that it does not force the multi user
interference to zero, instead the channel phase is fully compensated but the channel
amplitude is partially compensated [174]. In this technique coefficient Gm depends
on a parameter β as 𝐺 𝑚 =
𝐻 𝑚
∗
|𝐻 𝑚
∗ |1+𝛽, , −1 ≤ 𝛽 ≤ 1, where 𝐻 𝑚
∗
is the mth
channel co
efficient. Note that EGC, MRC and ORC can be viewed as particular cases for
which the parameter β assumes the values 0, −1 and 1 respectively. The key idea is
that if MRC and ORC are optimum in the extreme cases of noise limited and
interference limited system respectively then for each intermediate situation there
exists an optimum value of the parameter β which minimizes the mean bit error
probability (BEP) averaged over fading. The problem is to find the appropriate β
that minimizes the average bit error probability of the system and this system
compensates channel amplitude partially.
2.2.6 Pre Equalization
If information about the actual channel is a prior known at the transmitter,
pre equalization can be applied at the transmitter such that the signal at the receiver
appears non distorted and an estimation of the channel at the receiver is not
necessary. Information about the channel state can, for example, be made available
in Time division duplexing (TDD) schemes if the TDD slots are short enough such
that the channel of an uplink and a subsequent downlink slot can be considered as
constant and the transceiver can use the channel state information obtained from
previously received data.](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-45-320.jpg)
![26
thereby providing better balance between ISI mitigation and noise enhancement as
shown in Fig.2.3 [175-178].
Fig.2.3 Noise Enhancement in MMSE
Equalization according to the MMSE criterion minimizes the mean square
value of the Error between the transmitted signal and the output of the equalizer.
The mean square error
,l l l l lS G R
(2.6)
2
l lJ E
(2.7)
The mean square can be minimized by applying the orthogonality principle,
stating that the mean square error lJ is minimum if the equalizer coefficient ,l lG is
chosen such that the error l is orthogonal to the received signal
1
( ) ( )
0
k
k k
l
k
R H s n
i.e 0l lE R
The equalization coefficient based on the MMSE criterion is
,
, 2 2
,
l l
l l
l l
H
G
H
(2.8)
The computation of the MMSE equalization coefficients requires knowledge
about the actual variance of the noise 2
. For SNR→ ∞, the MMSE equalizer
becomes identical to the ZF equalizer. To overcome the additional complexity for
the estimation of 2
, a low complex sub optimum MMSE equalization can be
Noise Enhancement less than ZF
f
Information
f
Channel
f
Equalizer
f
Information
and NoiseNoise
f](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-49-320.jpg)
![30
where hn is the SC flat fading gain, φn is the SC fading phase and η(t) is AWGN with
single sided power spectral density N0. After phase compensation, the receiver
performs amplitude correction described by ( 1,.., )n cn N , called equalizer
coefficient. In the literature, different equalizer coefficient expressions for MMSE
have been proposed for MC CDMA system [171-173]. After demodulation the
received signal on the nth
SC is given by [176]
0
( )cos( )
sT
n n n n n ny r t t dt D I
(2.12)
where Dn is the desired signal and In is the multi user interference Components and
n
is the noise component with zero mean and variance 0
/ 4s
N T .Denoting 1, ,n nc ,
' ' ' '
1 2,[ , ..., ]cN , 1 2[ , ,... ] .c
T
Ny y y y After equalization and despreading, the
decision variable 'T
U y . Where
1
1
' y
yy b
R R
(2.13)
Let 1, 2,
[ ..., ]c
T
N
, 1, 2,[ ..., ]u
T
Nb b b b , d
C is a c uN N matrix with kth
column
being the spreading code for kth
user, and H is a diagonal matrix with nth
diagonal
element equals to n
h .
2
b s
c
E T
dN
y HC b
(2.14)
Then the Gaussian distribution of the matrix 1b yR is
1 1 2
b s
y c
E T
b dNR E b HC b
12
b s
c d
E T
N E b HC b
(2.15)](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-53-320.jpg)
![38
CHAPTER 3
PERFORMANCE OF MIMO MC CDMA SYSTEM WITH
MPCE BASED MMSE EQUALIZATION
3.1 INTRODUCTION
In wireless system, transmitted information reaches the receiver through a
radio channel. A channel can describe everything from the source to the sink of a
radio signal which includes the physical medium (free space, fiber, waveguides etc.)
between the transmitter and the receiver through which the signal propagates. The
signals from the transmitter is usually protected by the channel coding and
interleaved against fading phenomenon after which the binary signal is modulated
and transmitted over multipath fading channel. Due to the multipath channel signals
are distorted in a variety of ways like AWGN, amplitude, frequency and phase
distortion, resulting in ISI. In order to recover the original transmitted signal at the
receiver, it is essential to be aware of channel characteristics like reflection,
refraction and diffraction to ensure successful equalization and is possible with the
help of channel estimation. The general channel estimation procedure is shown in
Fig 3.1. Channel estimation is defined as the process of characterizing the effect of
physical channel on the input sequence. Channel estimation can be avoided by using
differential modulation technique, however such system result in low data rate and
there is a penalty of 3-4 dB less SNR [180-184]. In some cases channel estimation at
the user side can be avoided if the base station performs the channel estimation and
sends a pre distorted signal [185]. However, for fast varying channels, the pre
distorted signal might not bear the current channel distortion, causing system
degradation. Hence, system with channel estimation is needed for the future high
data rate system.](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-61-320.jpg)
![39
The CSI can be obtained through training based, blind and semi blind channel
estimation techniques. The blind channel estimation is carried out by evaluating the
statistical information of the channel and certain properties of the transmitted signals
[186]. It has no overhead but is applicable only to the slow fading channel due to its
need for a long data record. In training based channel estimation algorithm, training
symbols or pilot tones that are known prior to the receiver are multiplexed along with
the data stream for channel estimation [187]. Semi blind channel technique is a
combination of blind and training technique utilizing pilots and other natural
constraints to perform channel estimation.
Fig.3.1 Channel estimation procedure
3.2 TYPES OF CHANNEL ESTIMATION
This section discusses in detail the different channel estimation techniques.
3.2.1 Pilot Based Channel Estimation
The most preferred and used method to estimate the channel and the offset in
frequency is to use pilot symbols. Pilot symbols are known to the transmitter and
receiver in advance. The basic idea with pilot symbols is that there is a strong
correlation between the pilot symbol fading and the fading of data symbols that are sent
Channel
Estimated channel
model
Channel
Estimation
Technique
Transmitted
Sequence
x(n)
Estimated
signal
𝑌(n)
Error Signal
e(n)
Actual Received
signal Y(n)](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-62-320.jpg)
![40
close to the pilot symbol in time and SC. Different types of pilot arrangements are
discussed in literature such as block type pilot, group type pilot and irregular pilot
distribution [188-191]. In block type pilot based channel estimation shown in
Fig.3.2 (a), channel estimation symbols are transmitted periodically, in which all SCs
are used as pilots. If the channel is constant during the block, there will be no channel
estimation error since the pilots are sent in all carriers.
In comb type pilot based channel estimation shown in Fig.3.2 (b), an
efficient interpolation technique is necessary in order to estimate channel at data SC
by using the channel information at pilot SCs. In Fig.3.2 (a) and 3.2 (b) the pilots are
arranged in time and frequency domain respectively. In order to increase the channel
capacity irregular pilot arrangements [2, 192] discussed by authors is shown in the
Fig. 3.2 (c).
Fig.3.2 (a) Block type pilot arrangement
Fig.3.2 (b) Comb type pilot arrangement
Time
F
r
e
q
e
n
c
y
F
r
e
q
e
n
c
y
Time](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-63-320.jpg)
![41
Fig.3.2 (c) Irregular pilot arrangement
3.2.2 Blind Channel Estimation
In pilot or training based channel estimation the transmitter sends a signal
known prior to the receiver and is used for estimating the channel condition.
However, most wireless devices will be battery powered and the transmission of
training signals will seriously affect the robustness of such devices. Moreover,
training increases the overhead of the transmitted signal, reducing the net data
transmission rate. Thus, it is sensible to use blind channel estimation method that
possibly reduces the amount of training required significantly. Typically some
special property of the transmitted signal is exploited for blind channel estimation.
Blind equalization method provides attractive solution since they do not
require any known transmitted data for channel estimation and equalization purposes
[193-196] instead they use statistical and structural properties of the communication
signals. Channel identification or equalization requires information about both the
channel amplitude and phase responses that can be acquired from the received
signal. The higher order statistical properties gives phase information however
higher order statistics and spectra may not provide a feasible approach for
constructing practical equalizers. They have a large variance and consequently large
sample sets are needed in order to obtain reliable channel estimates. Even though
this is a drawback, can be used, in fast fading channel with high data rates and low
computational complexity requirement.
F
r
e
q
e
n
c
y
Time](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-64-320.jpg)
![42
3.2.3 Semi Blind Channel Estimation
Semi blind technique uses both pilot and natural constraints to estimate the
channel. These methods use pilots to obtain an initial channel estimate and improve
it by using a variety of prior information. Thus, in addition to the pilots, semi blind
methods use the cyclic prefix [197-199], time and frequency correlation [200],
Gaussian assumption on transmitted data [201] and virtual carriers [202] for channel
estimation and subsequent data detection.
3.3 CHANNEL ESTIMATION FOR OFDM SYSTEM
There are two main problems in designing channel estimators for wireless
OFDM system. The first problem is the arrangement of pilot information and the
second problem is the design of an estimator with both low complexity and good
channel tracking ability. In general, fading channel of the OFDM system can be
viewed as a two dimensional (2D) signal (time and frequency). The optimal channel
estimator in terms of mean square error is based on 2D Wiener filter interpolation.
Unfortunately, such a 2D estimator structure is too complex for practical
implementation due to need of power delay profile and as Doppler spread is not
known in wireless environment[252-254]. The combination of high data rate and
low bit error rate in OFDM system necessitates the use of estimators that have both
low complexity and high accuracy, where the two constraints work against each
other and a good tradeoff is needed. The one dimensional (1D) channel estimations
are usually adopted in OFDM system to accomplish the tradeoff between complexity
and accuracy [203-209].
The two basic 1D channel estimations are block type pilot channel estimation
and comb type pilot channel estimation in which the pilots are inserted in the
frequency and in the time domain as shown in Fig.3.3. The estimations for the block
type pilot arrangement can be based on least square (LS), MMSE and modified
MMSE. The estimations for the comb type pilot arrangement includes the LS
estimator with 1D interpolation, the maximum likelihood (ML) estimator, and the
parametric channel modeling based estimator. Other channel estimation such as](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-65-320.jpg)
![43
estimators based on simplified 2D interpolations, the estimators based on iterative
filtering and decoding, estimators for the OFDM system with multiple transmit and
receive antennas are studied in [210-213].
Fig.3.3 Block diagram of OFDM transceiver with pilot based channel
estimation
3.4 PILOT BASED CHANNEL ESTIMATION FOR MC CDMA SYSTEM
The block diagram of MC CDMA system incorporating channel estimation
and equalization is shown in Figs.3.4 and 3.5. In the transmitter after modulation the
data stream is spread by the wideband PN sequence to form the chip level transmit
matrix. The pilot signals are added for estimating channel response in the receiver
and modulated by the IFFT. Subsequently the output samples of the IFFT are
parallel to serial (P/S) converted to form the baseband signal which is added with
cyclic prefix (CP).
Upon receiving the signal, CP is removed and FFT is performed and the pilot
symbols are used for obtaining transfer function of the channel which in turn is used
for reducing the amplitude and phase distortion using equalization. With the
knowledge of estimated channel, data symbols are recovered after despreading the
signal with help of PN sequence.
AWGN
Data Out
Channel
Estimation
Mapping
Serial to
parallel
Pilot
insert
IFFT
Add Cyclic
Prefix
Parallel
to serial
Channel
Demap
Serial to
Parallel
Remove
Cyclic
Prefix
FFT
Pilot
Extract
Parallel
to Serial
Data In](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-66-320.jpg)
![59
CHAPTER 4
PERFORMANCE OF MIMO MC CDMA SYSTEM WITH MPCE
BASED MMSE EQUALIZATION AND ITBF
4.1 INTRODUCTION
In mobile environment the desired signal and the interference occupy the
same frequency band. Unless the signals have low correlation (like in CDMA
systems) temporal processing will not be effective in interference and noise
rejection. However the desired signal and interference arrive from different direction
and hence have different arrival time and angles of arrival. BF techniques are
designed to transmit or receive the signal to/from the preferred direction, where
multiple antennas are used to shape the overall antenna beam in a certain way to
maximize antenna gain in target direction and to suppress target dominant
interference. The main goal of BF is to increase received SNR and subsequently to
improve coverage. The useful signal in a targeted direction is enhanced by
constructive combination, whereas noise or interference are rejected by destructive
combination. For reducing ISI, channel estimation based equalization is a corrective
measure incorporated in the receiver as discussed in chapter 3. To reduce the
influence of ISI further and to improve the quality of received signal it is possible to
use BF technique as a preventive measure [249, 250].
The BF can be implemented both in transmitter and receiver through
multiple antennas [110,214]. For the receiver BF case, the received signal can be
optimally combined from different receiver antennas in order to maximize the
received SINR. The mean received SINR increases proportional to the number of
receiving antenna (Nd) and array gain by 10 log10(Nd) dB over the single antenna.](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-82-320.jpg)
![60
For the transmit BF case CSI is required and can be obtained from feedback
[92,95,215,216]. The transmit BF is based on perfect channel information that can
be designed to maximize the received SINR. The average received SINR increases
proportionally to the number of transmitting antenna(Ns) and array gain by
10 log10(Ns) dB over the single antenna. Transmit BF has attracted much attention
recently. By using partial or full knowledge of the CSI at the transmitter, transmit
BF can greatly enhance energy efficiency in wireless communications. On the
receive antenna, the received signal is,
1 2 1 2( )
x
y h h n h h x n
x
(4.1)
where y, is the received signal, hi are the channel coefficients seen from the ith
transmit antenna, x is the transmitted symbol and n is the noise at the receiver
antenna. When transmit BF is applied, the symbol from each transmit antenna
multiply with a complex number corresponding to the inverse of the phase of the
channel so as to ensure that the signals add constructively at the receiver. The
received signal can be expressed as
1
2
1 2
j
j
e
y h h x n
e
(4.2)
and the channel coefficients are further expressed as:
1
1 1
j
h e h
(4.3)
2
2 2
j
h e h
(4.4)
For applying BF to the simple system, set two BF parameters 1j
e
and 2j
e
the beam formed signal arrived at the receiver is modulus (to avoid phase difference)
of channel co-efficient and is expressed as 1 2( )y h h x n . It is to be noted that
the effective channel coefficients add in phase, so that the effect of multiple paths is
used to an advantage, ultimately improving the strength of the received signal.
4.2 TYPES OF BEAMFORMING](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-83-320.jpg)
![61
Beamforming can be classified as fixed beamforming, switched
beamforming, delay and sum beamforming, beam space beamforming and adaptive
beamforming.
4.2.1 Fixed Beamforming
In fixed BF the interference is not completely suppressed and also it does
not perform amplitude weighting of the received signal which can be realized by
adopting either an analog approach (e.g. switched beam, delay and sum) or a digital
approach (e.g. beam space BF) where the system can be usually realized at a
reasonable cost.
Switched Beamforming
In switched beam antennas the performance of the system can be improved
by multiple fixed beams which generate a finite number of radiation patterns, thus
making one RF signal available for each possible beam as shown in Fig.4.1. The
patterns are synthesized using an RF network that combines the signals received by
the N antennas in order to form the different beams targeted towards certain
directions. The RF network (Butler matrix) is realized adopting analog phase shifters
[217-219]. An RF switch selects the signal with the highest SINR from the set of
available outputs, which will be further processed by the Low Noise Amplifier.
Fig. 4.1 Switched Beamforming
Delay and sum beamforming
Butler
Matrix
RF Switch](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-84-320.jpg)
![62
The most ancient form of spatial filtering that adopts an analog approach is
the delay and sum BF, where delays are used instead of phase shifters as shown in
Fig. 4.2 [220]. For narrowband signals this scheme is substantially equivalent to
switched beam but the delay and sum technique can also be applied to broadband
signals. Depending on the delays, the antenna array isolates the signal for certain
directions but the interference is not specifically taken into account because this
scheme does not perform any amplitude weighting.
Fig.4.2 Delay and sum Beamforming
Beam space beamforming
The two previous methods are examples of analog fixed BF. Beam space,
instead, is a fixed BF technique that adopts a digital approach. In Fig 4.3 rather than
directly weighting the signals incoming on the array elements the antenna outputs
can be first processed in order to produce a set of orthogonal multiple beams.
Fig.4.3 Beam space Beamforming
4.2.2 Adaptive Beamforming
Output
SUM
Z1
Zn
SUM
DC & ADC
DC & ADC
Beam
Selection
Beam
Selection
X1
Xn
𝑥1
𝑥 𝑛](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-85-320.jpg)
![72
CHAPTER 5
PERFORMANCE OF MIMO MC CDMA SYSTEM WITH MPCE
BASED MMSE EQUALIZATION, ITBF AND NR
5.1 INTRODUCTION
Equalization employed in the receiver is a corrective measure to reduce ISI,
whereas BF is a preventive measure incorporated in the Transmitter. To further
improve the performance, relaying techniques are incorporated in the channel to
increase SNR, improve coverage, reduce power requirements at mobile stations and
will effectively reduce number of base station. The source (S) transmits to the
relay (R) nodes and the signal is retransmitted to the destination (D). When there is a
direct link between S and D, the D node will receive two replicas of the same data
transmitted through two different and independent channels: the S-R-D channel and
the S-D channel. The D node combines the direct signal in the first phase and the
relayed signals in the second phase to improve the SNR.
Since the R nodes are generally placed in different physical locations, a
diversity gain can be realized due to the spatial separation of the relays which would
enforce independent fading. Cooperative communication, an alternative technique to
provide impressive performance by exploiting spatial diversity in wireless
transmission. [231-233], has attracted researchers due to its ability to extend system
coverage, enhance spectrum efficiency and reduce the need for physical deployment
of more number of base station [250]. The basic idea of cooperative communication
is that all mobile users in a wireless network can help each other to send signals to
the D cooperatively. Thus, it is inherently more reliable for the destination to receive
the transmitted information, since from a statistical point of view, the chance of
having all transmission links to the destination failing is seldom.](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-95-320.jpg)
![74
Fig.5.2(a) Multi relay without direct link to D
Fig.5.2(b) Multi relay with direct link to D
5.2.3 MIMO Multi Relay System
The combination of relaying and MIMO techniques is advantageous as it
increases the data rate in the cellular edge and extends the network coverage.
Fig 5.3(a) shows MIMO relay networks which has been extensively investigated in
[221-228]. In addition, MIMO multi relay networks also has been studied in [229,
230, 247] and it is shown in the Fig 5.3(b).
Fig.5.3(a) MIMO relay with direct link to D
D
R1
RS
RL
S D
R
R
R
S D
R](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-97-320.jpg)
![75
Fig.5.3(b) MIMO Multi relay without direct link to D
The MIMO multi relay network consists of a single S and D node, both
equipped with multi antennas and relay network consisting of parallel relays with
multi antennas. Data transfer between S to D takes two non overlapping time slots
for the data to be transmitted from the S to the D via the S-R and R-D channels.
5.3 ONE WAY AND TWO WAY RELAY SYSTEM
In One way relay system, the information is always transmitted from the S
node and the D receives the signal from S. In two way scenario, the communication
is bi directional where two users exchange information. Under this scenario, each
node is not only a S, but also the intended D of the other node.
5.3.1 One Way Half Duplex
In half duplex transmission the data transmission will flow in one direction at
a time. Different modules of one way half duplex are as follows:
In module I, during the first time slot the S communicates with the R and D
(solid lines in Fig. 5.4(a)) and in the second time slot the R communicates with the
D (dashed line). This module shows the same structure as the Broadcast Channel
(BC) during the relay receive phase. This module is used by the cooperative
transmission defined in [221,234] and also in the relay assisted transmission
described in [235].
S D
R
R
R](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-98-320.jpg)
![80
the two sets of symbols containing the decoded data of the two transceivers. Each
transceiver subtracts its own data first and then decodes the data of the other user
[236]. In network coding, R combines the data from two terminals exploiting the
XOR operation where the combined data is re modulated and retransmitted. The two
terminals will find out the desired data by XOR ing the received data with its own
transmitted data [237, 238].
5.4 FULL DUPLEX TRANSMISSION
In full duplex mode shown in Fig.5.6, R amplifies and forwards the signals
to the destination while receiving the signals from S, which saves the time and space
resources of transmission and can obviously bring in an improvement of the
capacity. As transmission and reception is simultaneous on the same frequency in
full duplex mode, practical implementations suffer a self interference and signal
leakage between transmission and reception at R.
Fig.5.6 Full duplex module
5.5 CLASSIFICATION OF RELAY SCHEMES
Author‟s in [239] has discussed about the wide variety of relay schemes.
According to the processing functionality of relay node, the cooperative
communication scheme can be classified into Amplify and Forward (AF) [245,246] ,
Decode and Forward (DF) and compress and forward (CF)[240].
5.5.1 Amplify and Forward (AF) Relay
In AF scheme, R simply captures the signal from S, amplifies it, then
retransmits a noisy version of source transmission. Therefore, AF relay can be
considered transparent to modulation and coding techniques which are performed at
S and D. Since AF relay do not need decoding, it has low complexity transceivers
T R D](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-103-320.jpg)
![82
destination. Compared to DF relay, the noise and fading of the two hop channel is
accumulated with the desired signals along with the transmission path in AF relay
system. Although, AF relay does not suffer from the error propagation problem as
DF relay, no hard decision is performed on the received signal at R.
Fig. 5.7 Comparison of AF and DF relay
5.5.3 Compress and Forward (CF) Relay
Relays with CF module are also called Estimate and Forward (EF)
[241, 242], Observe and Forward (OF) [128], or Quantize and Forward (QF) [243].
CF is similar to DF module. But, unlike DF, CF goes beyond the quantization
process by applying S coding techniques on the received signal [244]. Thus, CF
module can be regarded as a hybrid solution of DF and AF modules. The received
signal is not decoded at R. But, the signal is quantized and source coded before it is
transmitted. So, the transmitted signal contains estimation errors. At the destination,
the relay estimation can be used as additional information while coding the signal
approaching through direct link.
5.6 MIMO OFDM SYSTEM WITH RELAY
The block diagram of MIMO OFDM transmitter and receiver using relay is
shown in Fig.5.8. The incoming data are first converted, coded and converted from a
serial bit streams into blocks of N complex signal samples. On each of these blocks,
an IFFT is performed. After parallel to serial conversion the data are transmitted to
S
AF
D
DF](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-105-320.jpg)
![85
S 𝐵(𝑛)
𝑦𝑑
(𝑛)
(𝑡2)
𝑦𝑑
(𝑛)
(𝑡1)
𝑦𝑟
𝑛
(t1) 𝑥 𝑟
𝑛
(t2)
𝐻𝑑
(𝑛)
𝐻𝑟
(𝑛)
𝐻𝑠
(𝑛)
Fig. 5.10 shows three nodes AF relay MIMO MC CDMA system where the
S node transmits information to the destination node through R node. The S, R, and
D node are equipped with Ns, Nr and Nd antennas, respectively. The communication
process between S and D node is completed in two time slot, to avoid the
interference between direct paths and R links. In the first time slot, the signal
sequence is modulated by NC subcarriers, ( )
, 1,.....,n
b cN n N as number of symbols
in the nth
subcarrier and it is transmitted to R as well as D. In second time slot the R
transmit the amplified version of signal to D.
Fig. 5.10 MIMO MC CDMA with three node AF relay
The received signal at R and D node can be expressed as [251]
( ) ( ) ( ) ( )
( ) ( ) ( ), 1,......n n n n
r s r c
y t H x t t n N
(5.1)
( ) ( ) ( ) ( )
( ) ( ) ( ), 1,......n n n n
d d d c
y t H x t t n N
(5.2)
where ( ) ( ) ( )
( ) ( ), 1,...... ,n n n
cx t P s t n N is the channel pre coding matrix for the S
signals at the nth
subcarriers. The ( )
( )n
ry t and ( )
( )n
r t are the received signal and the
noise vectors at the relay node, respectively, ( )
( )n
dy t and ( )
( )n
d t are the received signal
and the noise vectors at the destination node at time t, respectively. In the second
slot (t+1), the relay node amplifies the received signal vector at the nth
subcarrier
with an Nr × Nr matrix F(n)
and transmits the amplified signal vector to the
destination node.](https://image.slidesharecdn.com/performanceimprovementofmimomccdmasystemusingequalizationbeamformingandrelay-200815075325/85/Performance-improvement-of-mimo-mc-cdma-system-using-equalization-beamforming-and-relay-108-320.jpg)
![100
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Performance improvement of mimo mc cdma system using equalization, beamforming and relay
- 1. PERFORMANCE IMPROVEMENT OF MIMO MC CDMA SYSTEM USING EQUALIZATION, BEAMFORMING AND RELAY THESIS Submitted by N.TAMILARASAN In partial fulfilment for the award of the degree of DOCTOR OF PHILOSOPHY in ELECTRONICS AND COMMUNICATION ENGINEERING DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING PONDICHERRY ENGINEERING COLLEGE PONDICHERRY UNIVERSITY PUDUCHERRY- 605 014 INDIA JULY 2015
- 2. ii Dr. L.Nithyanandan Professor Department of Electronics and Communication Engineering Pondicherry Engineering College Puducherry – 605 014. CERTIFICATE Certified that this thesis entitled “PERFORMANCE IMPROVEMENT OF MIMO MC CDMA SYSTEM USING EQUALIZATION, BEAMFORMING AND RELAY” submitted for the award of the degree of DOCTOR OF PHILOSOPHY in ELECTRONICS AND COMMUNICATION ENGINEERING of the Pondicherry University, Puducherry is a record of original research work done by Shri. N. TAMILARASAN during the period of study under my supervision and that the thesis has not previously formed the basis for the award to the candidate of any Degree, Diploma, Associateship, Fellowship or other similar titles. This thesis represents independent work on the part of the candidate. Supervisor (Dr. L. Nithyanandan) Date: Place: Puducherry
- 3. iii ABSTRACT In wireless communication system, signals are transmitted through electromagnetic wave propagation in the atmosphere. The presence of reflectors in the surrounding of the transmitter and the receiver creates multiple paths through which signals are transmitted. The signals transmitted through these multiple paths often interfere with each other and receiver receives the superposition of multiple copies of the transmitted signal which elongates the symbol and results in inter symbol interference (ISI). Each signal copy will experience different attenuation, delay and phase shift while traveling from the source to the destination. This can result in either constructive or destructive interference which is referred as fading. To mitigate channel fading, diversity techniques which transmit and process multiple copies of the same signal through different fading channel is often employed. At the receiver, these different copies of the signal are effectively combined to reconstruct the complete transmitted signal. The multi carrier Code division multiple access (MC CDMA) combined with themulti input and multi output (MIMO) technique has become a core technology for future mobile radio communication system to cater millions and millions of users with enriched services. Multipath propagation, mobility of transmitter, receiver and local scattering cause the signal to spread in frequency, time and angle, resulting in ISI in the received signal. Possible potential gain in spectral efficiency is challenged by the receiver’s ability to accurately detect the symbol in the presence of ISI. Eventhough MC CDMA mitigates the problem of time dispersion, still it is necessary to effectively remove the amplitude/phase shift caused by channel and to solve this problem,channel estimation based equalization in the receiver, beam forming (BF) in the transmitter and relay in channel can be invoked.
- 4. iv The ISI arising in the multipath fading channels has been recognized as the major obstacle for high speed data transmission over wireless channel. Equalization technique is an effective means for reducing the influence of ISI. Several combining and equalization techniques for orthogonal frequency division multiplexing (OFDM), MC CDMA and MIMO signals have appeared in the literature. The equal gain combining(EGC), maximum ratio combining (MRC) and orthogonality restoring combining (ORC) suitable for single carrier communication, if used in multi carrier system, reduces the orthogonality of user creating additional interference in the receiver. Among the various equalization methods for OFDM, MC CDMA and MIMO, minimum mean square error (MMSE) is considered to be a good solution for data recovery inspite of its complexity. MMSE can effectively utilize the diversity of the frequency selective channel and reduce the ISI. The performance of proposed MIMO MC CDMA system with MMSE equalization is compared with MC CDMA and MIMO MC CDMA without equalization. Further the performance of the system is tested with different antenna configuration, subcarriers (SCs) and different modulations. The MMSE equalization alone is not an efficient way of reduction of ISI, as the equalization is not carried out with the knowledge of channel impairments. To strengthen the effect of equalization for reducing ISI, channel estimation is used to estimate the amplitude/phase shift caused by the wireless channel impairments. A modified pilot channel estimation (MPCE) is proposed for MIMO MC CDMA system in which the number and position of pilots are varied dynamically based on the channel condition. The advantage of MPCE based MMSE equalization is the reduction of ISI with optimal pilots so as to reduce spectral loss. The MPCE based MMSE equalization removes the effect of the wireless channel and ensures effective symbol demodulation. Apart from MPCE based MMSE equalization in the receiver, improved transmit BF(ITBF) is incorporated as a preventive measure where array of antennas are “directed” at a desired target or source by adjusting the relative gain and phase of the array elements. By this, the antenna pattern, or beam, can be made to point in a favoured direction and to attenuate other directions in order to reduce the effect of
- 5. v interference source. BF improves reception quality and increases throughput in a MIMO MC CDMA communication system. ITBF is made more effective with the knowledge of channel information obtained from the MPCE. To further improve the system performance, novel relays (NR) are utilized for contiguous coverage of areas with high traffic density. A traditional approach would be to increase the base station density. However, the network cost scales linearly with the amount of deployed BSs whereas the amount of users and the average revenue per user will not increase accordingly. Therefore, cost efficient alternative deployment concepts are needed. One promising alternative deployment is a novel relay with MPCE based equalization and ITBF to extend the high throughput coverage of next generation mobile networks. To summarize, this work proposes different techniques in the transmitter, receiver and the channel to enhance the performance of MIMO MC CDMA system through ISI reduction so that apart from increasing the capacity, quality is also ensured.
- 6. vi ACKNOWLEDGEMENT With immense pleasure and deep sense of gratitude, I would like to place on record my thanks to all those who have contributed to the successful completion of this research work. I am duty bound to express my deep indebtness to my supervisor and mentor, Dr. L. Nithyanandan, Professor, Department of Electronics and Communication Engineering, for his unflinching support, selfless motivation and contagious enthusiasm from the inception of the research to the culmination stage. As my supervisor, he has constantly forced me to remain focused on achieving my goal. His observations and comments helped me to establish the overall direction of the research and to move forward with investigation in depth. I express my ingenious, sincere requital to Doctoral committee member Dr. P. Dananjayan, Professor, Department of Electronics and Communication Engineering, Pondicherry Engineering College for his immense help, unswerving motivation and lively deliberation inspite of his busy schedule. I express my profound gratitude and allegiance to Doctoral committee member Dr. K. Porsezian, Professor, Department of Physics, Pondicherry University, for his magnanimous benignity and benevolence, which has enthused me to work harder and achieve the goal. I am grateful to Dr. D. Govindarajulu, Principal, Pondicherry Engineering College for his whole hearted support and for permitting me to make use of the facilities in the college for the research work. I deem it a privilege to record my sincere thanks to Dr. V. Prithiviraj, Former Principal, Pondicherry Engineering College, who has taught me to think ahead and encouraged me. I take this opportunity to thank Dr. G.Nagarajan, Professor and Head, Department of Electronics and Communication Engineering and Dr. E Srinivasan,
- 7. vii Former Head of the Department for allowing me to utilize the facilities in the department for my work as and when needed. I am also thankful for their encouragement throughout my research work. I also thank, the Chairman and Principal of Shri Krishna College of Engineering and Technology, Puducherry for their support and encouragement and love given by them immensely during my research work. With deep sense of gratitude, I wish to express my sincere thanks to my God in heaven Thiru J. Narayanasamy and my Goddess in earth Mrs. Lakshmi Kandham Narayanasamy for the motivation provided. My profound gratitude is to my wife, Mrs. R. Ranjani Tamilarasan and my child T.R. Tharanerajaan for their patience, kind heartedness and moral support offered during the crucial period of my research N. TAMILARASAN
- 8. viii TABLE OF CONTENTS CHAPTER NO. TITLE PAGE NO. ABSTRACT iii ACKNOWLEDGEMENT vi LIST OF TABLES xiii LIST OF FIGURES xiv LIST OF ABBREVIATIONS xviii LIST OF SYMBOLS xxi 1 INTRODUCTION 1 1.1 GENERAL 1 1.2 NEED FOR MIMO MC CDMA SYSTEM 2 1.2.1 CDMA 3 1.2.2 OFDM 4 1.2.3 Multicarrier CDMA 7 1.2.4 MIMO 8 1.3 LITERATURE SURVEY 9 1.4 OBJECTIVE OF THE RESEARCH WORK 17 1.5 THESIS ORGANIZATION 17 2 PERFORMANCE OF MIMO MC CDMA SYSTEM WITH MMSE EQUALIZATION 19 2.1 INTRODUCTION 19 2.2 DIFFERENT TYPES OF EQUALIZATION TECHNIQUE 20 2.2.1 Equal Gain Combining (EGC) 20 2.2.2 Maximum Ratio Combining (MRC) 20 2.2.3 Orthogonality restoring combining (ORC) 21 2.2.4 Threshold ORC (TORC) 21
- 9. ix CHAPTER NO. TITLE PAGE NO. 2.2.5 Partial Equalization(PE) 22 2.2.6 Pre Equalization 22 2.2.7 Combined Equalization 23 2.2.8 Maximum Likelihood Sequence Estimator (MLSE) 23 2.2.9 Maximum Likelihood Symbol by Symbol Estimation (MLSSE) 24 2.2.10 Maximum Likelihood Detection 25 2.2.11 Minimum Mean Square Equalizer (MMSE) 25 2.3 MIMO MC CDMA SYSTEM WITH MMSE EQUALIZATION 27 2.4 SIMULATION RESULTS AND DISCUSSION 31 2.4.1 Performance of the System with MMSE Equalization (BPSK) 32 2.4.2 Performance of the System with MMSE Equalization (QPSK) 35 2.5 SUMMARY 37 3 PERFORMANCE OF MIMO MC CDMA SYSTEM WITH MPCE BASED MMSE EQUALIZATION 38 3.1 INTRODUCTION 38 3.2 TYPES OF CHANNEL ESTIMATION 39 3.2.1 Pilot Based Channel Estimation 39 3.2.2 Blind Channel Estimation 41 3.2.3 Semi Blind Channel Estimation 42 3.3 CHANNEL ESTIMATION FOR OFDM SYSTEM 42 3.4 PILOT BASED CHANNEL ESTIMATION FOR MC CDMA SYSTEM 43
- 10. x CHAPTER NO. TITLE PAGE NO. 3.5 MODIFIED PILOT CHANNEL ESTIMATION (MPCE) BASED MMSE EQUALIZATION FOR MIMO MC CDMA SYSTEM 44 3.6 SIMULATION RESULTS AND DISCUSSION 48 3.6.1 Performance of the System with MPCE based MMSE Equalization (BPSK) 49 3.6.2 Performance of the system with MPCE based MMSE Equalization (QPSK) 51 3.6.3 Performance of the system with MPCE based MMSE Equalization (16 QAM) 54 3.7 SUMMARY 58 4 PERFORMANCE OF MIMO MC CDMA SYSTEM WITH MPCE BASED MMSE EQUALIZATION AND ITBF 59 4.1 INTRODUCTION 59 4.2 TYPES OF BEAMFORMING 61 4.2.1 Fixed Beamforming 61 4.2.2 Adaptive Beamforming 63 4.3 MIMO MC CDMA SYSTEM WITH MPCE BASED MMSE EQUALIZATION AND ITBF 63 4.4 SIMULATION RESULTS AND DISCUSSION 66 4.4.1 Performance of the System with MPCE based MMSE and ITBF (QPSK) 66 4.4.2 Performance of the System with MPCE based MMSE and ITBF (16 QAM) 67 4.5 SUMMARY 71
- 11. xi CHAPTER NO. TITLE PAGE NO. 5 PERFORMANCE OF MIMO MC CDMA SYSTEM WITH MPCE BASED MMSE EQUALIZATION, ITBF AND NR 72 5.1 INTRODUCTION 72 5.2 CLASSIFICATION OF RELAY SYSTEM 73 5.2.1 Single Relay System 73 5.2.2 Multiple Relay System 73 5.2.3 MIMO Multi Relay System 74 5.3 ONE WAY AND TWOWAY RELAY SYSTEM 75 5.3.1 One Way Half Duplex Mode 75 5.3.2 Two Way Half Duplex Mode 77 5.4 FULL DUPLEX TRANSMISSION 80 5.5 CLASSIFICATION OF RELAY SCHEMES 80 5.5.1 Amplify and Forward (AF) Relay 80 5.5.2 Decode and Forward (DF) Relay 81 5.5.3 Compress and Forward (CF) Relay 82 5.6 MIMO OFDM SYSTEM WITH RELAY 82 5.7 MIMO MC CDMA SYSTEM WITH MPCE BASED MMSE EQUALIZATION, ITBF AND NR 83 5.8 SIMULATION RESULTS AND DISCUSSION 87 5.8.1 Performance of the System with MPCE based MMSE, ITBF and NR (QPSK) 88 5.8.2 Performance of the System with MPCE based MMSE, ITBFand NR (16QAM) 90 5.9 SUMMARY 95
- 12. xii CHAPTER NO. TITLE PAGE NO. 6 CONCLUSION 96 6.1 RESEARCH CONTRIBUTIONS 97 6.2 SCOPE FOR FURTHER WORK 99 REFERENCES 100 LIST OF PUBLICATIONS 126 VITAE 127
- 13. xiii LIST OF TABLES TABLE NO. TITLE PAGE NO. 2.1 Simulation parameter of the system with MMSE equalization 31 2.2 Impact of MMSE equalization (BPSK) at target BER of 10-3 34 2.3 Impact of MMSE equalization (QPSK) at target BER of 10-3 37 3.1 Simulation parameters of the system with MPCE based MMSE equalization 48 3.2 Impact of MPCE based MMSE equalization (BPSK) at target BER of 10-3 51 3.3 Impact of MPCE based MMSE equalization (QPSK) at target BER of 10-3 53 3.4 Impact of MPCE based MMSE equalization (16QAM) at target BER of 10-3 57 4.1 Simulation parameters of the system with MPCE based MMSE and ITBF 66 4.2 Impact of MPCE based MMSE and ITBF at target BER of 10-3 (QPSK) 67 4.3 Impact of MPCE based MMSE and ITBF at target BER of 10-3 (16 QAM) 71 5.1 Simulation parameter of the system with MPCE based MMSE, ITBF and NR 88 5.2 Impact of MPCE based MMSE, ITBF and NR (QPSK) at the target BER of 10-3 89 5.3 Impact of MPCE based MMSE, ITBF and NR (16 QAM) at the target BER of 10-3 95
- 14. xiv LIST OF FIGURES FIGURE NO. TITLE PAGE NO. 1.1 Code division multiple access 4 1.2 Comparison of the bandwidth utilization for FDM and OFDM 5 1.3 OFDM transmitter 6 1.4 OFDM receiver 6 1.5 A simple MC CDMA transmitter 7 1.6 A simple MC CDMA receiver 8 1.7 Multiple Input Multiple Output antenna system 8 2.1 Example of ISI on received pulse in a Communication system 19 2.2 Noise Enhancement in ORC 21 2.3 Noise Enhancement in MMSE 26 2.4 Simplified diagram of MMSE equalizer 28 2.5 Block diagram of MIMO MC CDMA Transmitter 29 2.6 Block diagram of MIMO MC CDMA Receiver with MMSE Equalization 29 2.7 Performance of the system with/without MMSE (BPSK, 16 SCs) 32 2.8 Performance of the system with/without MMSE (BPSK, 64 SCs) 33 2.9 Performance of the system with/without MMSE (BPSK, 128 SCs) 33 2.10 Performance of the system with/without MMSE (QPSK, 16 SCs) 35 2.11 Performance of the system with/without MMSE (QPSK, 64 SCs) 36 2.12 Performance of the system with/without MMSE (QPSK, 128 SCs) 36
- 15. xv FIGURE NO. TITLE PAGE NO. 3.1 Channel estimation procedure 39 3.2(a) Block type pilot arrangement 40 3.2(b) Comb type pilot arrangement 40 3.2(c) Irregular pilot arrangement 41 3.3 Block diagram of OFDM transceiver with pilot based channel estimation 43 3.4 Block diagram of MC CDMA transmitter with pilot signal 44 3.5 Block diagram of MC CDMA receiver with pilot based channel estimation 44 3.6 (a) Flow chart of MPCE (Delay spread) (b) MPCE (Doppler spread) 45 3.7 Block diagram of modified pilot insertion based transmitter 46 3.8 Block diagram of MPCE based MMSE equalization 46 3.9 MPCE estimation procedure 47 3.10 Performance of the system with MPCE based MMSE (BPSK, 16 SCs) 49 3.11 Performance of the system with MPCE based MMSE (BPSK, 64 SCs) 50 3.12 Performance of the system with MPCE based MMSE (BPSK, 128 SCs) 50 3.13 Performance of the system with MPCE based MMSE (QPSK, 16 SCs) 52 3.14 Performance of the system with MPCE based MMSE (QPSK, 64 SCs) 52 3.15 Performance of the system with MPCE based MMSE (QPSK, 128 SCs) 53 3.16 Performance of the system with MPCE based MMSE (2x2, 16 SCs, 16 QAM) 54 3.17 Performance of the system with MPCE based MMSE (2x2, 64 SCs, 16 QAM) 55
- 16. xvi FIGURE NO. TITLE PAGE NO. 3.18 Performance of the system with MPCE based MMSE (2x2, 128 SCs, 16 QAM) 55 3.19 Performance of the system with MPCE based MMSE (4x4, 16 SCs, 16 QAM) 56 3.20 Performance of the system with MPCE based MMSE (4x4, 64 SCs, 16 QAM) 56 3.21 Performance of the system with MPCE based MMSE (4x4, 128 SCs, 16 QAM) 57 4.1 Switched Beamforming 61 4.2 Delay and sum Beamforming 62 4.3 Beam space Beamforming 62 4.4 Flowchart of ITBF 64 4.5 Block diagram of MIMO MC CDMA system with MPCE based MMSE and ITBF 65 4.6 Performance of 4 x 4 antennas configuration (128 SCs, QPSK) 67 4.7 Performance of 2 x 2 antennas configuration (16 SCs, 16 QAM) 68 4.8 Performance of 2 x 2 antennas configuration (64 SCs, 16 QAM) 68 4.9 Performance of 2 x 2 antennas configuration (128 SCs,16 QAM)69 4.10 Performance of 4 x 4 antennas configuration (16 SCs, 16 QAM) 69 4.11 Performance of 4 x 4 antennas configuration (64 SCs, 16 QAM) 70 4.12 Performance of 4 x 4 antennas configuration (128 SCs, 16 QAM)70 5.1 Simple three node relay 73 5.2(a) Multi relay without direct link to D 74 5.2(b) Multi relay with direct link to D 74 5.3(a) MIMO relay with direct link to D 74 5.3(b) MIMO Multi relay without direct link to D 75 5.4(a) One way half duplex module 1 76 5.4(b) One way half duplex module II 76 5.4(c) One way half duplex module III 76 5.4(d) One way half duplex module IV( Forwarding) 77
- 17. xvii FIGURE NO. TITLE PAGE NO. 5.5(a) Two way half duplex module I 78 5.5(b) Two way half duplex module II 78 5.5(c) Two way half duplex module III 79 5.5(d) Two way half duplex module IV 79 5.6 Full duplex module 80 5.7 Comparison of AF and DF relay 82 5.8 Block Diagram of MIMO OFDM transmitter and receiver with Relay 83 5.9 Block diagram of MIMO MC CDMA system with MPCE based MMSE, ITBF and NR 84 5.10 MIMO MCCDMA with three Node AF relay 85 5.11 Performance of 4x1x 4 antenna configuration for 128 SCs (QPSK) 88 5.12 Performance of 4 x 2 x 4 antenna configuration for 128 SCs (QPSK) 89 5.13 Performance of 2 x1x 2 antenna configuration for 64 SCs (16 QAM) 91 5.14 Performance of 2 x2x 2 antenna configuration for 64 SCs (16 QAM) 91 5.15 Performance of 2 x1x 2 antenna configuration for 128 SCs (16 QAM) 92 5.16 Performance of 2 x2x 2 antenna configuration for 128 SCs (16 QAM) 92 5.17 Performance of 4 x1x 4 antenna configuration for 64 SCs (16 QAM) 93 5.18 Performance of 4 x 2 x 4 antenna configuration for 64 SCs (16 QAM) 93 5.19 Performance of 4 x1x 4 antenna configuration for 128 SCs (16 QAM) 94 5.20 Performance of 4 x2x 4 antenna configuration for 128 SCs (16 QAM) 94
- 18. xviii LIST OF ABBREVIATIONS AF Amplify and Forward ANC Analog Network Coding AWGN Additive White Gaussian Noise BC Broadcast Channel BEP Bit Error Probability BER Bit Error Rate BF Beam Forming BPSK Binary Phase Shift keying CDMA Code Division Multiple Access CP Cyclic Prefix CSI Channel State Information D Destination DF Decode and Forward DFT Discrete Fourier Transform DS Direct Sequence DVB Digital Video Broadcasting Eb/No Energy per Bits to Spectral Noise Density EDGE Enhanced Data Rates for GSM Evolution EF Estimate and Forward EGC Equal Gain Combining FDM Frequency Division Multiplexing FDMA Frequency Division Multiple Access FFT Fast Fourier Transform GPRS General Packet Radio Service GSM Global System for Mobile Communication HDTV High Definition Television ICE Ideal Channel Estimation IFFT Inverse Fast Fourier Transform IP Internet Packet
- 19. xix ISI Inter Symbol Interference ITBF Improved Transmit BF ITU(T) International Telecommunication Union (Technical) LS Least Square MAI Multi Access Interference MAP Maximum a Posteriori MC CDMA Multicarrier Code Division Multiple Access MIMO Multi Input and Multi Output MISO Multiple Input Single Output ML Maximum likelihood MLSE Maximum Likelihood Sequence Estimator MLSSE Maximum Likelihood Symbol by Symbol Estimation MMS Multimedia Messaging Service MMSE Minimum Mean Square Error MMSEC Minimum Mean Square Error Combining MPCE Modified Pilot Channel Estimation MRC Maximum Ratio Combining MSNR Maximum Signal to Noise Ratio MT Multi Tone NR Novel relay OF Observe and Forward OFDM Orthogonal Frequency Division Multiplexing ORC Orthogonality Restoring Combining P/S Parallel to Serial PE Partial equalization PN Pseudo Noise QF Quantize and Forward QPSK Phase Shift Keying R Relay RBF Random BF S Source S/P Serial to Parallel
- 20. xx SC Subcarrier SIMO Single Input Multiple Output SINR Maximum Signal to Interference Noise Ratio SISO Single Input Single Output SNR Signal to Noise Ratio STC Space Time Coding TDD Time Division Duplexing TDMA Time Division Multiple Access TORC Threshold ORC WCDMA Wideband Code Division Multiple Access ZF Zero Forcing 1D One dimensional 1G First Generation 2D Two dimensional 2G Second Generation 3G Third Generation 4G Fourth Generation
- 21. xxi LIST OF SYMBOLS B(n) Beam form vector bk(i) ith transmitted data bit of k th user cn Spreading code d Data sequence d(k) Data symbol of user k dµ Data symbols vectors Dn Desired signal E{.} Expectation Eb Bit energy f Frequency F(n) Amplified signal vector Gl,l lth diagonal element of the equalizer matrix G Gm Subcarrier weighting coefficient H Diagonal matrix 𝐻 𝑚 ∗ mth channel coefficient 𝐻𝑠 𝑛 Channel matrix between source and relay 𝐻(k) Channel transfer function of k th user Hd Channel matrix between source and destination hi Channel coefficient of ith transmit antenna Hl,l lth diagonal element of the channel matrix H hn Subcarrier flat fading gain Hr Channel matrix between relay and destination ηr(t) Noise vector of the relay at time t In Multi user interference 𝐼 𝑁 𝐶 Identity matrix M Number of data symbols transmitted N Processing gain N l lth element of the noise vector n
- 22. xxii N0 Single sided power spectral density Nb Number of symbols in nth Subcarrier Nc Subcarrier Nd Destination or Receiving antenna Nr Relay antenna Ns Source ortransmitting antenna P(n) Precoding matrix for source signal P{.} Probability r Received vector r(t) Received signal at time t Rl lth element of the received vector r S l lth element of the vector S S(n) (t) Signal vector at time t S(t) Transmitter signal at time t T Transpose T Bit duration Ts Symbol duration uTs(t) Rectangular waveform with unit amplitude X(k) Transmitted data samples x(n) (t) Precoded signal vector at time t x(t) Channel precoding matrix for source xr(t2) Amplified signal of relay at t2 Y(k) Received signal yd(t) Received signal at the destination at time t YP(k) Received pilot signals yr(t) Received signal at the relay at time t (.)* Complex conjugation (n) nth Subcarrier ||.|| Norm of a vector ∆2 (.,.) Squared Euclidean distance αn Equalizer co efficient β Equalization parameter with value in the range of [-1,1]
- 23. xxiii η(t) Additive white Gaussian noise ηd(t) Noise vector of the destination at time t ηn Noise component λ Threshold σ2 Variance of the noise φn Subcarrier fading phase ωn Radian frequency of nth subcarrier
- 24. 1 CHAPTER 1 INTRODUCTION 1.1 GENERAL Wireless technologies have evolved remarkably since Guglielmo Marconi first demonstrated radio transmission in the year 1896 by controlling an electric bell through a remote arrangement. The radio transmission has been very much useful for the people to communicate with each other and has timely access to information regardless of the location of individuals. This is supported by the advances in the wireless networking technology and portable computing devices with reduction in the size of physical device leading to the rapid development in mobile communication infrastructure. The first generation (1G) has been an analog based FDMA (Frequency division multiple access) system for voice transmission only. The 2G cellular systems uses time division multiple access (TDMA) based digital modulation schemes with source and error correction coding techniques to improve user capacity, voice quality and spectrum efficiency. The second generation (2G) systems include Global System for Mobile communications (GSM), IS-54, IS-136, IS-95 and Japan Digital Cellular. These systems were able to perfectly provide basic services and when demand for variety of wideband services increased the evolution towards the 3G started. The first step in this direction has been commonly accepted as 2.5G with the development of new technologies based on the classical GSM (e.g., General Packet Radio Service (GPRS) and Enhanced Data Rates for Global Evolution (EDGE)).The real third generation (3G) mobile system have been developed to offer both low and high bandwidth services like telephony, Internet access and multimedia at any time and from anywhere through a single device. The first 3G
- 25. 2 system based on the Wideband Code Division Multiple Access (WCDMA) transmission protocol offer wideband services, such as wireless Internet services (with peak rate of 384 Kbps) and video transmissions (with data rate up to 2Mbps). As 3G technology and its first evolution 3.5G congested in few years, telecommunication research community started working for the fourth generation (4G) wireless communication. 4G solution is based on full IP convergence system to meet the needs of high performance applications like wireless broadband access, Multimedia Messaging Service, video chat, mobile TV, High Definition Television (HDTV) content and Digital Video Broadcasting (DVB).The goal is to have data rates in the range of 100-500 Mbps with premium quality and high security. In order to realize this, it is necessary to tackle the hostile physical properties of wireless channel in the form of rapid time variation, fading (large scale and small scale), multipath propagation and channel interference. The interference created by user of different cells using the same resource is called co-channel interference and interference resulting from users of same cell is called adjacent cell interference. Counter measures were employed to combat these impairments through spreading, space diversity and frequency diversity. Therefore MIMO MC CDMA system is standardized by international telecommunication unit (ITU(T)) for 4G and beyond 4G system. 1.2 NEED FOR MIMO MC CDMA SYSTEM The performance of current wireless communication system degrades due to excessive multi path propagation and Doppler spread. Due to delay spread bit arrives the receiver at different times because of the different paths taken causing bit overlap and interferes with each other resulting in ISI, which leads to distortion of signals causing bit error at the receiver. As the data rate is increased the transmission time for individual pulse decreases, become narrow and effect of channel delay spread increases. ISI has been recognized as the major obstacle for high speed data transmission over multipath wireless channel. OFDM, a multi carrier modulation scheme splits high data rate streams into a number of lower rate streams that are transmitted simultaneously over a number of SC. Since the symbol duration increases in the lower rate parallel SCs, the relative
- 26. 3 amount of dispersion in time caused by multipath delay spread decreases. Although OFDM is robust to frequency selective fading, it has severe disadvantages in SC synchronization and sensitivity to frequency offset estimation. The other main issue of OFDM is the presence of a large number of SCs which exhibits a non constant nature in its envelope. Future generation is to cater more users with enriched services, CDMA has a strong candidate to support multimedia mobile services as opposed in conventional schemes such as TDMA/FDMA which also has the ability to cope up with the asynchronous nature of the multimedia traffic. By employing Rake receivers CDMA systems can coherently combine the multipath components due to the hostile frequency selective channel and the processing gain due to spreading provides robustness to the multi user interference. The use of conventional CDMA does not seem to be realistic when the data rates go up to a hundred megabits per second due to severe ISI(inter symbol interference) and the difficulty in synchronizing a fast sequence. Further to enhance the performance, the MIMO antennas at both ends of a wireless link helps to realize high data rate through spatial multiplexing and drastically improve the spectral efficiency of the system and provide link reliability in rich scattering environments through spatial diversity. In addition, the capacity of MIMO system increases linearly with the number of transmit receive antenna pairs without increasing transmitted power. Even though multicarrier modulation and MIMO mitigate ISI, effectiveness of this suppression is limited and both of these two techniques cannot work perfectly in fast fading environment [1-3]. To overcome the problem, this work proposes equalization and channel estimation in the receiver BF in the transmitter and relay in the network to reduce the ISI, cater more number of users and improve the performance. 1.2.1 CDMA In CDMA the information signals of different users are modulated by orthogonal or non orthogonal spreading codes. The resulting spread signals simultaneously occupy the same time and bandwidth, as shown in Fig.1.1. The
- 27. 4 receiver uses the spreading code structure to separate out the different users. This method has the feature that the unwanted signals with different code will get spread even more by the process and appear like noise to the receiver. Some of the properties that have made CDMA useful are: signal hiding, non interference with existing systems, anti jam, interference rejection, information security and multipath tolerance. Though CDMA ensure effective utilization of bandwidth, it is not capable of transmitting data at rates of around 5 to 10 Mbps. Various studies indicate that some sort of multi carrier modulation (MCM) scheme can only support higher data rates and be suitable for mobile paths. Fig.1.1 Code division multiple access 1.2.2 OFDM OFDM is the basic MCM scheme that has been extremely used for high data rate application. In OFDM, the available spectrum is divided into many carriers, each one being modulated by a low rate data stream. Like FDMA, in OFDM also [2-4] the multiple user access is achieved by subdividing the available bandwidth into multiple narrow band channels, which are allocated to users. However, OFDM uses the spectrum much more efficiently by spacing the channels much closer together with no overhead as in FDMA. This is achieved by making all the carriers orthogonal to one another, thus preventing interference between the closely spaced carriers as shown in Fig. 1.2. Time Frequency
- 28. 5 Fig.1.2 Comparison of the bandwidth utilization for FDM and OFDM Each carrier in an OFDM signal has a very narrow bandwidth (i.e. 1 kHz), thus the resulting symbol rate is low. This result in the signal having a high tolerance to multipath delay spread, as the delay spread must be long enough to cause significant inter symbol interference (e.g. > 100 ms). Fig.1.3 and 1.4 show a simple OFDM transmitter and OFDM receiver respectively. OFDM has a serious limitation of being sensitive to nonlinear amplification, frequency and phase errors in SCs. Moreover the mobile community may not be interested in loosing the merits of CDMA and therefore possibilities of mixing CDMA and MCM have been initiated. Researchers have analyzed three different combinations of OFDM and CDMA; multi carrier (MC) CDMA, MC direct sequence (DS) CDMA and multi tone (MT) CDMA [5], out of which MC CDMA is chosen as possible access scheme for beyond 3G mobile communication systems. Frequency Conventional Frequency Division Multiplex (FDM) Frequency Saving of bandwidth Orthogonal Frequency Division Multiplex (OFDM)
- 29. 6 Fig.1.3 OFDM transmitter Fig.1.4 OFDM receiver S/P Data Symbols 1/ √T (1/ √T) e j 2π t / T (1/ √T) e j 2π (N-1) t / T OFDM Signal P/S ∫T ∫T ∫T 1/ √T OFDM Signal (1/ √T) e – j 2π / T (1/ √T) e - j 2π (N-1) t / T Data Symbols
- 30. 7 1.2.3 Multicarrier CDMA The MC CDMA transmitter spreads the original data stream in the frequency domain over different SCs [6] using a given spreading code as depicted in Fig. 1.5. In this system the SCs convey the same information at one time. The binary data dk are first multiplied by the spreading code (PN binary sequences) ck(i) and the bit duration T of the data is much longer than the PN sequence chip duration Tc. Fig.1.5 A simple MC CDMA transmitter Normally N=T/TC may be equal to 32, 64, 128 or higher. After the necessary encoding, the sequence that corresponds to one data bit is modulated in the band of N modulators. A very efficient way of realizing such a modulation scheme is through the inverse fast fourier transform (IFFT). The distance between SC is equal to the chip rate or its multiple. The guard time/ cyclic prefix should be introduced for each symbol in order to combat the inter symbol interference caused by the channel and filters in the system. At the output of IFFT the pulse shaping filter followed by a modulator shifts the whole spectrum to the desirable frequency band. Another solution is to apply the offset IFFT that eliminates the necessity of the above mentioned frequency shift. In the MC CDMA system, SC frequencies are usually chosen to be orthogonal to each other, i.e., SC frequencies attempt to satisfy the following condition: ∑dk (t) ck (1) ck (N) exp (j 2πf1t) exp (j 2πfNt) sk (t)
- 31. 8 0 cos( ) cos( ) 0, Tc i jt t dt for i j (1.1) where TC is the chip duration, ωi and ωj are ith and jth carrier frequencies respectively. The basic structure of the MC CDMA mobile station receiver shown in Fig. 1.6 contains the same blocks as that of OFDM receiver. However, the output of fast fourier transform (FFT) is fed to k branches (one branch for each user) containing an equalizer and a despreading block to equalize the individual channel and receive the signal using the user‟s unique PN code. Fig.1.6 A simple MC CDMA receiver 1.2.4 MIMO To improve channel throughput and robustness of a radio link, multiple antennas are employed at both the transmitter and the receiver which is known as MIMO shown in Fig.1.7. A MIMO system with similar count of antennas at both the transmitter and the receiver is able to multiply the system throughput linearly with every additional antenna. MIMO often employs Spatial Multiplexing to enable signal (coded and modulated data stream) to be transmitted across different spatial domains. Fig.1.7 Multiple Input Multiple Output antenna system Tx Rx ∫T (.) ∑ ∫T (.) exp (-j 2πf1t) exp (-j 2πfNt) ck (1) ck (N) r(t) d k, n
- 32. 9 The use of multiple antennas in the receiver and/or in the transmitter basically provides the following advantages [7]. Array gain: Array gain is an increase in average received SNR obtained by coherently combined signals transmitted from multiple antennas or received by multiple antennas. In order to get array gain channel knowledge at the transmitter or receiver is required. Diversity gain: Diversity gain refers to improved link reliability in space, time, frequency or polarization. Diversity gain can be exploited in the case of independently fading multiple channels experienced by different antennas in order to mitigate the effect of multipath fading Spatial multiplexing gain: Multiplexing gain corresponds to the increased data rate due to the ability to transmit multiple parallel data streams over MIMO channel without increasing the bandwidth or total transmit power. The capability to support multiple streams depends on the rank of the channel matrix H. As a result, the MIMO technology can be used to achieve improved system performance, such as increased spectral efficiency, increased user throughput and better coverage. 1.3 LITERATURE SURVEY Today‟s wireless communication requires high data rate and good quality, which is possible only if the received signal is not affected by channel impairments. The diversity combining schemes are applied to combine the multipath received signals of a diversity reception device into a single improved signal. These schemes are applied at the receiver to mitigate the effect of channel fading on the received signal to obtain the signal with maximum signal to noise ratio (SNR).
- 33. 10 Various types of combating techniques for channel impairments are proposed in the literature based on noise, interference and capacity for OFDM, MC CDMA and MIMO system. The signals of different SCs are appropriately weighted and summed using EGC [8-11], MRC [12-15], ORC or zero forcing (ZF) [16-18] and threshold based ORC (TORC) [19]. The MRC technique represents the optimal choice when the system is noise limited; in contrast, when the system is interference limited, the ORC can completely eliminate MAI in the downlink transmission; yet, it causes noise amplification in the receiver [20]. The MMSE [21-26] criterion may also be used to derive the equalizer coefficients, whereas an even more powerful optimization criterion is the minimum bit error ratio (MBER) criterion [27]. A lot of research studies have been dedicated to the error performance evaluation of MC CDMA systems [28-31], and the approximation of the bit error rate (BER) for MC CDMA with MRC, EGC, and MMSEC has been derived [28]. However, it is based on the law of large numbers, i.e. the spreading code length must be sufficiently large. In [29], the authors have derived the exact BER of a synchronous MC CDMA system with MRC. The BER is calculated based on a moment generating function method with no assumption on the distribution of multiple access interference. The BER performance of MC CDMA with MRC and EGC has been evaluated over a Rayleigh fading channel with correlated envelopes and phases [30]. A lower bound and a tight approximation on the BER of MC CDMA with ORC is presented in [31].Although MRC, EGC and ORC only requires the channel state information (CSI), the MMSE and MBER equalizers are more complex because they exploit additional knowledge of SNR. Partial equalization (PE) [32-34] is another technique to reduce the ISI, where the signals are partially equalized to avoid noise and interference in the receiver instead of complete equalization. A different approach has been proposed in [35,36] in the form of combined equalization. This technique applies pre equalization at the transmitter in conjunction with post equalization at the receiver which requires channel knowledge at both the transmitter and receiver ends increasing the complexity of the system. In [37] combined partial equalization was discussed where partial equalization was considered at the transmitter and TORC at
- 34. 11 the receiver. From the literature survey it is observed that MMSE equalization is good choice inspite of its complexity. Many researchers concentrate only on equalization but not on channel estimation. CSI is a prime factor for equalization to reduce the amplitude and phase distortion. Differential demodulation without channel estimation was used in [1] which results in SNR loss of upto 3 dB. The channel tracking method using kalman filter has been analyzed which results in better estimation accuracy but suffers from computational complexity and delay [38, 39]. Various authors have reported the effect of channel estimation error and phase noise [40-43]. Some authors have tried channel estimation using known data at the receiver. It involves sending pilots (symbols which are known to the receiver) with the data symbols so that the channel can be estimated and therefore the data at the receiver. Training sequence and optimal training based channel estimation is discussed for MIMO system [44,45] in which with increasing number of transmitting and receiving antennas, the length of the training sequence needs to be increased, leaving less space for data transmission and reduced overall spectral efficiency. However the optimal choice of training sequence is quite challenging. In [46, 47] the channel estimation is totally avoided by introducing differential modulation at the expense of a performance penalty, however the spectral efficiency of MIMO system depends upon the channel estimation. Most channel estimation methods for OFDM transmission systems have been developed under the assumption of a slow fading channel, where the channel transfer function is assumed stationary within one OFDM data block. In addition the channel transfer function for the previous OFDM data block is used as the transfer function for the present data block. In practice, the channel transfer function of a wide band radio channel may have significant changes even within one OFDM data block. Therefore, it is preferable to estimate channel characteristics based on the pilot signal in each individual OFDM data block [48-50].
- 35. 12 To increase the spectral efficiency less number of pilots operating in time varying channels [51], dynamic pilot symbol arrangement with wiener filter [52] is proposed. Channel estimation technique for MIMO OFDM is studied [53, 54], concentrating only on OFDM but not on MIMO system. To avoid increasing the length of pilot, optimal/training based channel estimation is discussed for MIMO OFDM system [55-58], and MIMO CDMA [59] but real challenge is in arriving the optimal number of pilots Incorporating pilots decreases the spectral efficiency of the system and they usually require averaging over many symbols before obtaining a good estimate which make the scheme inefficient for high speed data communication. The limitations in training based estimation techniques motivated interest in the spectrally efficient blind approach [60-71]. They utilize certain underlying mathematical information about the kind of data being transmitted. Existing blind methods can be broadly categorized as statistical or deterministic: The former methods rely on assumptions on the statistics of the input sequence [60], while the latter make no such assumption [61]. In the first category, i.e., statistical approach, blind channel estimation using second order statistics can potentially achieve superior performance for a given time averaging interval than approaches based on higher order statistics [62]. The second category, i.e., deterministic method, is generally favored when the input statistics are unknown, or there may not be sufficient time samples to estimate them. Several interesting deterministic methods were discussed in [63]; however most of them are exclusively for SISO or single carrier transmissions. Amid second order statistics based blind approaches, subspace based estimation are attractive since estimates can often be obtained in a simple form by optimizing a quadratic cost function [64]. Without employing any precoder, a subspace based method is proposed for OFDM systems by utilizing the redundancy introduced by the cyclic prefix (CP) [65], and it is further extended for MIMO OFDM systems in [66]. Virtual carriers (VCs) are SCs that are set to zero with no information being transmitted, which can be used for channel estimation. Such a
- 36. 13 scheme is proposed for OFDM systems [67] and further extended to MIMO OFDM systems in [68]. Variants of the statistics based methods for example by inserting zero padding instead of CP for each OFDM block or by introducing the so called repetition index and remodulation on the received signal [69-71]. However, the number of required time samples is still implicitly proportional to the size of the IFFT in the OFDM modulator. It is also noted that deterministic approaches still need to accumulate data samples to algebraically obtain channel estimates, and their performance in noise improves as the number of samples increases. Therefore, as the dimension of the parameter space is increased in the MIMO OFDM context, the number of samples required for deterministic methods to achieve an acceptable level of performance will also inevitably be increased. Eventhough these methods are bandwidth efficient but are notoriously slow to converge, extremely computationally intensive and hence are impractical to implement in real time systems. Channel estimation based on semi blind techniques are proposed in order to combine the advantage of pilot based channel estimation and semi blind approach [72-76]. These methods use pilots to obtain initial CSI and blind approach methods are used to improve the system performance. However obtaining optimal value of pilot signals are difficult. In [73] authors develop a semi blind channel estimation method for MIMO OFDM systems based on a specific and non redundant precoding scheme, say, circular precoding, since the circular precoding allows channel estimation at the receiver and simplifies the encoding scheme at the transmitter. In [76], an efficient semi blind sparse channel estimation approach for MIMO OFDM systems was proposed. Through the analysis of the second order statistics of the received signal that passes through a sparse channel, a blind algorithm for obtaining a constraint on the sparse channel vector with respect to the MSTs is developed. By combining this blind constraint with a training based sparse LS criterion, a semi blind solution to the estimation of the effective channel is then obtained.
- 37. 14 It is possible to use the CSI for BF also, a representative MIMO technique which is a promising solution that could be utilized to enhance throughput [77-80]. There are many available beam design algorithms for MIMO interference channels such as interference alignment based algorithms [81-83] and sum rate targeted algorithms [81-86]. However, most of these algorithms assume perfect CSI at transmitters and receivers, whereas the assumption of perfect CSI is unrealistic in practical wireless communication systems since perfect CSI is hard to obtain practically due to channel estimation error, limited feedback or other limitations [87]. Thus, the CSI error should be incorporated into the beam design to yield better performance, and this is typically done under robust beam design frameworks. One approach for multi antenna channels [88-90] is based on quantization of the instantaneous CSI at the receiver followed by its conveyance to the transmitter using a low rate feedback channel. This is a limited feedback approach, there are also other approaches [91-93] which are based on feedback of partial (or statistical) channel information. These methods, in general, do not perform well as the ones using instantaneous feedback since they do not track the rapid fluctuations of the channel. There are more general feedback schemes [94-96] that dynamically adapt to the distribution of the channel, but these methods are generally too complicated to implement in practice. In [95-97] and [98-104], the MIMO BF vector is quantized at the receiver using a fixed codebook available at both the transmitter and the receiver. There are two challenges in designing a MIMO BF system using quantized feedback. One is how to design a good codebook, and the other is to decide on a BF weight vector in a codebook efficiently. The unitary DFT matrix codebook and the Grassmannian codebook were proposed in [97] and [89], respectively. In [101], an iterative search algorithm that has linear complexity on average with respect to the number of transmit antennas was proposed. Random BF(RBF) has been widely studied due to its simplicity and lower feedback overhead, although its performance is poorer than that of other orthogonal BF schemes that utilize full channel information [105-108]. Since RBF uses only SINR feedback rather than the full channel information, the feedback overhead can be efficiently reduced.
- 38. 15 In MIMO OFDM, BF is performed independently on each SCs, which is referred to as subcarrier wise beamforming [109,110], to exploit the spatial diversity. Unfortunately, subcarrier wise beamforming induces high computational complexity since a discrete fourier transform (DFT) operation is required for each antenna element [111,112]. In addition, the optimized transmit weight vector for every SC has to be fed back to the transmitter, which leads to the growth of feedback overhead linearly in proportion to the number of SC. To reduce the overall complexity and the feedback overhead, several schemes have been proposed for OFDM systems with single or multiple antennas in the literature [111-115] and can be classified as pre DFT beamforming or symbol wise beamforming which increase the complexity of implementation because of BF operation in both transmitter and receiver. The distributed BF schemes presented in [116-118] assume that the transmitter, the receiver and the relay nodes all use a single antenna. As a result, these schemes do not benefit from spatial processing at the nodes. Relays can be deployed in wireless networks to extend coverage and improve throughput[119]. Relays can be either full duplex or half duplex. Full duplex relays can transmit and receive at the same time while half duplex relays cannot. Because full duplex relays are difficult to implement, practical systems focus on half duplex relays [120,121]. Relays are also classified based on how they process the received signal. The most popular relay types are decode and forward (DF) [122-124] and amplify and forward (AF) [125-127]. DF relays have higher computational complexity due to the requirement of decoding the signals and are helpful only if they can decode successfully the signals. Transparent to the modulation and coding of the signals, AF relays can be used flexibly in heterogeneous networks comprising of many nodes of different complexity or standards [114]. AF relays provide better diversity performance and in some cases higher throughput (for example, when the source relay channel is weak) than DF relays in single antenna half duplex relay systems [128,129]. AF relays may be attractive in practice due their lower complexity and faster signal processing. MIMO techniques can be incorporated into relay systems by equipping each node with multiple antennas to provide multiplexing and diversity gains [130].
- 39. 16 Transceiver designs for traditional one way MIMO relay systems were proposed in [131-135,155]. The optimal two way relay strategies were studied in [136,157] to maximize achievable sum rate and to achieve optimal diversity multiplexing tradeoff. For an analog network coding (ANC) based MIMO two way relay system consisting of a multi antenna relay node and two single antenna source nodes, the optimal relay BF was studied in [137,156] to characterize the achievable capacity region, and also to minimize the mean squared error [138]. In [139] and [140], the authors considered jointly optimizing the source and relay pre coders to maximize sum rate for MIMO two way relay systems where all nodes are equipped with multiple antennas. The joint optimization problem was also investigated in [141] based on the MMSE criterion, and an iterative algorithm as well as a heuristic algorithm based on channel parallelization was proposed therein. In [142] and [143], the authors addressed joint source and relay optimization for multiple relay systems. All of the above mentioned works assume perfect CSI is available. In practice, CSI has to be estimated using pilot symbols, and channel estimation errors are inevitable due to noise and the time varying nature of wireless channels. Hence, it is desirable to provide robustness against channel uncertainties when designing transceiver. Further Capacity bounds for MIMO relay channels are studied in the literature [144,136]. Automatic repeat request techniques for MIMO relays are proposed to achieve the optimal diversity multiplexing delay tradeoff [145] and to improve throughput and outage probability performances [146]. MIMO relay with multiuser linear precoding schemes in fixed relay networks have been considered in [147,148] where CSI is assumed to be known at the transmitter. Relay gain matrix optimization was studied in [131,132] to maximize the source destination capacity and linear transceiver designs were proposed based on the MMSE criterion [135]. Full duplex relay discussed for MC CDMA system [149,150] is difficult to implement due to the generation of self interference and different types of relaying technique for MIMO OFDMA system is discussed in [151-154]. However, most of MIMO relay techniques require high complex equalizers and also error free forwarding at the relay to enjoy the diversity, which is impractical and thus may not be suitable for low complexity communication environments.
- 40. 17 1.4 OBJECTIVE OF THE RESEARCH WORK Globally the demand for wireless application is increasing every year which necessitates the design of progressive and perfect transmission of multimedia data over wireless multipath channels. Even though, MIMO MC CDMA is chosen as the access technique for realizing the same, quality offered by this technique fails to meet the demands of future wireless communication system. To improve the performance of the system, this work proposes the MPCE based MMSE equalization, ITBF and NR to meet the quality of wireless communication such as high transmission rate, more transmission range and transmission reliability. 1.5 THESIS ORGANIZATION Chapter 1 provides an overview of CDMA, OFDM and MIMO techniques. The objective of the research work, state of art survey relevant to the topic of research and author‟s contribution in the related areas are also given. Organization of the thesis is presented at the end of this chapter. Chapter 2 discusses the different combining and equalization techniques. The simulated BER performance of MIMO MC CDMA system with/without proposed MMSE equalization under ideal channel estimation(ICE) for different antenna configuration, SC and modulation are explained and performance of the proposed system is compared with the existing MC CDMA system. Chapter 3 deals with different channel estimation techniques and importance of channel estimation for OFDM, MIMO OFDM and MIMO MC CDMA system. Simulations are carried out for MIMO MC CDMA with MPCE based MMSE for different antenna configuration, SC and modulation and the results are discussed in detail. Chapter 4 highlights BF techniques. BER Performance of MPCE based MMSE equalization and ITBF for proposed MIMO MC CDMA is simulated and compared with different modulation.
- 41. 18 Different types of relaying performance improvement through proposed NR technique is discussed in chapter 5. Impact of MPCE based MMSE equalization and ITBF with NR on BER for MIMO MC CDMA is investigated through simulation. Chapter 6 concludes the thesis by emphasizing the major aspects of the study. A summary of major contribution and scope for further work is also included in this chapter.
- 42. 19 CHAPTER 2 PERFORMANCE OF MIMO MC CDMA SYSTEM WITH MMSE EQUALIZATION 2.1 INTRODUCTION Wireless communication system requires signal processing technique that improves the link performance in hostile mobile radio environment. The mobile radio channel is particularly dynamic due to multipath propagation and Doppler spread. From Fig.2.1 it can be noted that rectangular multilevel pulses passing through the channel will spread in time and the pulse for each symbol smears into adjacent time slots due to multipath time delay spread causing ISI resulting in an irreducible BER floor for mobile system. However, even if a mobile channel is not frequency selective, the time varying Doppler spread due to motion creates an irreducible BER that can be realized over a frequency selective channel. Hence ISI has been recognized as the major obstacle to high speed data transmission over wireless channel. Fig.2.1 Example of ISI on received pulse in a Communication system 0 0 t t Ts Sampling points Individual pulse response (Sum of pulse) Inter symbol Interference Sampling points t t0 0 Ts Individual pulse response 0 0 1 0 0 0 1 0 0 0 Input Waveform Sampling points t t
- 43. 20 The use of OFDM and cyclic prefix mitigates the effect of time dispersion. However, it is still necessary to remove the amplitude and phase shift caused by multipath effect on the channel. The effect of ISI can be counteracted by different measures such as time or frequency domain equalization. The function of equalizer within a receiver is to compensate for the average range of expected channel amplitude and delay variations, also it may be adaptive as the channel is generally unknown and time varying. 2.2 DIFFERENT TYPES OF EQUALIZATION TECHNIQUE Many different detection schemes have been proposed in the literature for OFDM and MC CDMA signal detection [158,159,179]. Every detection technique is characterized by its specific combining parameters. The basic detection technique treat the signals from other users as interference and their combining parameters are functions of multipath fading channel. 2.2.1 Equal Gain Combining (EGC) The EGC weights equally each sub channel contribution and compensate only the phases. In this case SCs are processed with the same amplification and correlation is performed by the means of Walsh matrix. The EGC method would be a suitable approach for an additive white Gaussian noise (AWGN) channel where all transfer factors are equal but in a fading channel the transfer factors of the SCs have different amplitudes [160-162]. For this reason the orthogonality of the Hadamard codes is violated and interference between the information symbols in the received signal creates large error floor. 2.2.2 Maximum Ratio Combining (MRC) In this method the signals from all the SCs are weighted according to their individual signal power to noise power ratios and then summed. The individual signal should be cophased before being summed which generally requires individual receiver and phasing circuit [163-170]. MRC produces an output SNR equal to sum of the individual SNRs. Thus it has an advantage of producing an output with an
- 44. 21 acceptable SNR even when none of the individual signals are themselves acceptable. But this method performs well only when the signals are quite above noise level. In this technique, if fading occurs in any one of the branches, a maximum gain is applied to it which enhances the noise level and totally induce non orthogonality between the codes. 2.2.3 Orthogonality Restoring Combining (ORC) While EGC may be desirable for simplicity and MRC for combating noise, neither of these techniques significantly exploit the coding of the SC. Good choice is to restore the orthogonality between the sequence through ORC, which implies a total cancellation of multiuser interference; but this method enhances the noise because the sub channels with low SNR have higher weights as shown in Fig.2.2.[171-173]. Fig.2.2 Noise Enhancement in ORC 2.2.4 Threshold Orc (TORC) With TORC (or Controlled Equalization), an attempt at restoring the orthogonality between users is made by normalizing the amplitudes of the SCs. As the orthogonality of the users are encoded in the phase of the SCs, it is primarily beneficial in the downlink where phase distortion for all users may be corrected and in this, SCs above a certain threshold will be equalized and retained. This constraint is added to prevent the amplification of SCs with small amplitudes that may be dominated by a noise component. A threshold for amplitude of the transfer factor is introduced to suppress the weak SCs. The bit error rate depends on the Noise Enhancement f Channel Noise f Equalizer f Information f Information and Noise f
- 45. 22 noise level and on interference due to SCs below the threshold. Both effects show a different behavior. Interference depends on the probability of rejecting a SC and dearly rises with threshold. The interference is especially responsible for the error floor at high SNR. The noise level is reduced with rising threshold because fewer weak SCs are accepted. At moderate and low thresholds the average noise level is increased hence it is difficult to determine the optimum threshold in fast fading. 2.2.5 Partial Equalization (PE) An equalizer with partial channel amplitude compensation is similar in structure to a ORC. The only difference is that it does not force the multi user interference to zero, instead the channel phase is fully compensated but the channel amplitude is partially compensated [174]. In this technique coefficient Gm depends on a parameter β as 𝐺 𝑚 = 𝐻 𝑚 ∗ |𝐻 𝑚 ∗ |1+𝛽, , −1 ≤ 𝛽 ≤ 1, where 𝐻 𝑚 ∗ is the mth channel co efficient. Note that EGC, MRC and ORC can be viewed as particular cases for which the parameter β assumes the values 0, −1 and 1 respectively. The key idea is that if MRC and ORC are optimum in the extreme cases of noise limited and interference limited system respectively then for each intermediate situation there exists an optimum value of the parameter β which minimizes the mean bit error probability (BEP) averaged over fading. The problem is to find the appropriate β that minimizes the average bit error probability of the system and this system compensates channel amplitude partially. 2.2.6 Pre Equalization If information about the actual channel is a prior known at the transmitter, pre equalization can be applied at the transmitter such that the signal at the receiver appears non distorted and an estimation of the channel at the receiver is not necessary. Information about the channel state can, for example, be made available in Time division duplexing (TDD) schemes if the TDD slots are short enough such that the channel of an uplink and a subsequent downlink slot can be considered as constant and the transceiver can use the channel state information obtained from previously received data.
- 46. 23 An application scenario of pre equalization in a TDD mobile radio system would be that the terminal station send pilot symbol in the uplink which are used in the base station for channel estimation and detection of the uplink data symbol. The estimated channel state is used for pre equalization of the downlink data to be transmitted to the terminal station. Thus, no channel estimation is necessary in the terminal station which reduces its complexity. Only the base station has to estimate the channel, i.e. the complexity can be shifted to the base station. A further application scenario of pre equalization in a TDD mobile radio system would be that the base station send pilot symbol in the downlink to the terminal station, which performs channel estimation. In the uplink, the terminal station applies pre equalization with the intention to get quasi orthogonal user signals at the base station receiver antenna. This results in a high spectral efficiency in the uplink, since MAI can be avoided. Moreover, complex uplink channel estimation is not necessary. The accuracy of pre equalization can be increased by using prediction of the channel state in the transmitter where channel state information from the past is filtered. 2.2.7 Combined Equalization With combined equalization CSI is available at both the transmitter and receiver. This enables pre equalization to be applied at the transmitter together with post equalization at the receiver. The knowledge about the transmission channel can be made available, for example, by exploiting TDD to gather CSI at transmitter needed for pre equalization, and by performing channel estimation at Rx in order to obtain CSI needed for post equalization. 2.2.8 Maximum Likelihood Sequence Estimator (MLSE) The optimal equalizer with highest probability correctly detects the transmitted sequence which is the maximum likelihood sequence estimator (MLSE). It minimizes the sequence error probability, i.e. the data symbol vector error probability, which is equivalent to maximizing the conditional probability |dP r
- 47. 24 that d was transmitted given the received vector r. The estimate of d obtained with MLSE is |P r dd=arg max d (2.1) with arg denoting the argument of the function. If the noise lN is additive white Gaussian, Equation (2.1) is equivalent to finding the data symbol vector d that minimizes the Squared Euclidean distance between the received and all possible transmitted sequences. 2 2 ( , ) || ||r r A d d (2.2) The most likely transmitted data vector is 2 min ( , )r dd=arg d (2.3) MLSE requires the evaluation of MK squared Euclidean distances for the estimation of the data symbol vector d which will increase the complexity of the system, if the number of user and data is increased. 2.2.9 Maximum Likelihood Symbol by Symbol Estimation (MLSSE) The Maximum Likelihood Symbol by Symbol Estimation (MLSSE) minimizes the symbol error probability, which is equivalent to maximizing the conditional probability ( ) |k P d r that ( )k d was transmitted given the received sequence r. The estimate of ( )ˆ k d obtained by MLSSE is ( ) ( ) ( )ˆ |k k k d P d d =arg max r (2.4)
- 48. 25 If the noise lN is additive white Gaussian the most likely transmitted data symbol is ( ) ( ) ( ) 2 2 1ˆ exp ( , )k k k d with same reelization of d r d d =arg max d (2.5) The increased complexity with MLSSE compared to MLSE can be observed in the comparison of (2.5) with (2.3). An advantage of MLSSE compared to MLSE is that MLSSE inherently generates reliability information for detected data symbols that can be exploited in a subsequent soft decision channel decoder. 2.2.10 Maximum Likelihood Detection The optimum multi user detection technique exploits the maximum a posteriori (MAP) criterion or the maximum likelihood criterion. In the previous section, two optimum maximum likelihood detection algorithms are shown, namely the MLSE, which optimally estimates the transmitted data sequence (0) (1) ( 1) ( , ,...... )k T d d d d and the MLSSE, which optimally estimates the transmitted data symbol d(k) . It is straightforward that both algorithms can be extended to a MAP sequence estimator and to a MAP symbol by symbol estimator by taking into account the a priori probability of the transmitted sequence and symbol respectively. When all possible transmitted sequences and symbols respectively are equally probable a priori, the estimator based on the MAP criterion and the one based on the maximum likelihood criterion are identical. The possible transmitted data symbol vectors are , 0,..... 1K M d , where MK is the number of possible transmitted data symbol vectors and M is the number of possible realizations of d(k) . 2.2.11 Minimum Mean Square Equalizer (MMSE) The ORC equalizer cancels all ISI but can lead to considerable noise enhancement. The MMSE technique minimizes the expected mean squared error between the transmitted symbol and the symbol detected at the equalizer output,
- 49. 26 thereby providing better balance between ISI mitigation and noise enhancement as shown in Fig.2.3 [175-178]. Fig.2.3 Noise Enhancement in MMSE Equalization according to the MMSE criterion minimizes the mean square value of the Error between the transmitted signal and the output of the equalizer. The mean square error ,l l l l lS G R (2.6) 2 l lJ E (2.7) The mean square can be minimized by applying the orthogonality principle, stating that the mean square error lJ is minimum if the equalizer coefficient ,l lG is chosen such that the error l is orthogonal to the received signal 1 ( ) ( ) 0 k k k l k R H s n i.e 0l lE R The equalization coefficient based on the MMSE criterion is , , 2 2 , l l l l l l H G H (2.8) The computation of the MMSE equalization coefficients requires knowledge about the actual variance of the noise 2 . For SNR→ ∞, the MMSE equalizer becomes identical to the ZF equalizer. To overcome the additional complexity for the estimation of 2 , a low complex sub optimum MMSE equalization can be Noise Enhancement less than ZF f Information f Channel f Equalizer f Information and NoiseNoise f
- 50. 27 realized. With sub optimum MMSE equalization, the equalization coefficients are designed such that they perform optimally only in the most critical cases for which successful transmission should be guaranteed. The variance 2 is set equal to a threshold λ at which the optimal MMSE equalization guarantees the maximum acceptable BER. The equalization coefficient with sub optimal MMSE equalization results in and requires only information about ,l lH . The value λ has to be determined during the system design. Among the various equalization methods for MIMO and MC CDMA, MMSE is considered to be a good solution for data recovery since it can effectively reduce the ISI and utilize the diversity of the frequency selective channel. , , 2 , l l l l l l H G H (2.9) 2.3 MIMO MC CDMA SYSTEM WITH MMSE EQUALIZATION From the literature survey it is observed that equalization under ICE is carried out for OFDM and MC CDMA using single antenna. It has been observed that in comparison with conventional single antenna system, significant capacity gain is achievable when multiple antennas are used both at the transmitter and receiver. Conventional equalizer techniques like EGC, MRC and ORC are suitable for single carrier communication only. OFDM is the multicarrier technique used in MIMO MC CDMA system and it depends on the orthogonality among SCs for bandwidth saving and proper detection. Incorporating multiple antennas with multiple paths causes different phase/amplitude shifts and loss of orthogonality. EGC, MRC and ORC equalizer cannot be used for MIMO MC CDMA system as these equalizer reduces the orthogonality of user thus creating additional interference in the receiver and using ORC for MIMO will also increase noise in the receiver. Increased noise/Interference weakens wireless signal which will affect the quality of the link. In this work MMSE equalizer is proposed for MIMO MC CDMA system, the simplified diagram of which is given in Fig.2.4. It is to be noted, the novelty of this equalizer is that it considers different antenna signals as different links basically and further different path of different links are considered while equalizing the signal, which proves to be effective in counteracting the amplitude/phase shift.
- 51. 28 Fig.2.4 Simplified diagram of MMSE equalizer Figs. 2.5 and 2.6 show the proposed model of MIMO MC CDMA transmitter and receiver. In this system the signal from the users are multiplexed at the transmitter and transmitted as one entity through the radio channel. In the transmitter, data is first modulated using Binary phase shift keying (BPSK) or quadrature phase shift keying (QPSK) modulation, multiplied by the spreading sequence of the user and then modulated using an OFDM modulator. In the receiver, the signals are received through multiple antennas and demodulated using FFT. After OFDM demodulation the amplitude and phase distortions are adjusted using MMSE equalization where the ISI is reduced improving the performance of the system and the user data symbols are recovered by despreading with corresponding spreading codes. Consider a MC CDMA system having Nc SCs and s d N N MIMO system, the transmitted signal after modulation can be expressed as 2 1 1 ( ) ( ) ( )cos( ) s c b sc s N N E k n T s nN T i k n s t b i c u t iT t (2.10) where Eb and Ts are the bit energy and symbol duration respectively, uTs(t) represents a rectangular waveform with unit amplitude and pulse duration Ts, bk(i) is the ith transmitted data bit cn is the spreading code, S N is the source(S) (or transmitting) antenna, ωn = 2πf0+ 2π(n - 1)∆f is the frequency in radians of the nth SC, and the frequency spacing is ∆f= 1/Ts. The received signal r(t) through destination(D)(or receiving )antenna d N is given by 2 1 1 . ( )cos( )( ) ( ) ( ) s d c b c s T s n n N N E n k nN T i k n u t iT tr t t h b i c (2.11) Transmitter Receiver with MMSE
- 52. 29 Fig. 2.5 Block diagram of MIMO MC CDMA transmitter Fig. 2.6 Block diagram of MIMO MC CDMA receiver with MMSE equalization MappingData CP Insertion IFFT P/S PN Sequence S/P PN Sequence Detector CP Removal FFTP/S MMSE Equalization Estimated Data S/P 29
- 53. 30 where hn is the SC flat fading gain, φn is the SC fading phase and η(t) is AWGN with single sided power spectral density N0. After phase compensation, the receiver performs amplitude correction described by ( 1,.., )n cn N , called equalizer coefficient. In the literature, different equalizer coefficient expressions for MMSE have been proposed for MC CDMA system [171-173]. After demodulation the received signal on the nth SC is given by [176] 0 ( )cos( ) sT n n n n n ny r t t dt D I (2.12) where Dn is the desired signal and In is the multi user interference Components and n is the noise component with zero mean and variance 0 / 4s N T .Denoting 1, ,n nc , ' ' ' ' 1 2,[ , ..., ]cN , 1 2[ , ,... ] .c T Ny y y y After equalization and despreading, the decision variable 'T U y . Where 1 1 ' y yy b R R (2.13) Let 1, 2, [ ..., ]c T N , 1, 2,[ ..., ]u T Nb b b b , d C is a c uN N matrix with kth column being the spreading code for kth user, and H is a diagonal matrix with nth diagonal element equals to n h . 2 b s c E T dN y HC b (2.14) Then the Gaussian distribution of the matrix 1b yR is 1 1 2 b s y c E T b dNR E b HC b 12 b s c d E T N E b HC b (2.15)
- 54. 31 The matrix yy R is 2 4 b s o s cc T d d E T N T NN HC C H I (2.16) where cNI is an identity with c cN N .Substituting (2.15) and (2.16) into (2.13), the set of equalizer coefficients for MMSE scheme for downlink as 12 2 1 . c c c cb s b o T d d N N N n N nE T E N n HC C H I h (2.17) 2.4 SIMULATION RESULTS AND DISCUSSION Simulation of MC CDMA and MIMO MC CDMA system are performed using MATLAB version 9.1 with BPSK/QPSK modulation and the simulation parameter is shown in Table 2.1. The system is tested using 16, 64 and 128 SCs. To understand the impact of MIMO and equalization, simulations are carried out for 2x2, 3x3 and 4x4 transmitting and receiving antenna respectively with and without MMSE equalization Table 2.1 Simulation Parameter of the system with MMSE Equalization Spreading Codes Walsh Hadamard Code Number of SCs 16/ 64 /128 Channel Rayleigh fading Modulation BPSK / QPSK Antennas 2x2/ 3x3/ 4x4 Equalization Technique MMSE
- 55. 32 2.4.1 Performance of the system with MMSE equalization (BPSK) Figs. 2.7, 2.8 and 2.9 show the BER performance of MIMO MC CDMA system with MMSE and without MMSE for BPSK modulation with different antenna configuration and SCs. In Fig. 2.7 the legend graph illustrates the BER of MIMO MC CDMA without using MMSE and the line graph shows the BER of MIMO MC CDMA using MMSE for 2x2 and 4x4 antennas. It can be inferred from the graph equalization improves the performance due to the effective compensation of amplitude/phase shifts caused by different links/different path and subsequent reduction of ISI. It can also be noted that the increase in number of antennas improves the performance due to the spatial diversity and multiplexing gain. Fig.2.7 Performance of the system with/without MMSE (BPSK, 16 SCs) 0 5 10 15 10 -4 10 -3 10 -2 10 -1 10 0 E b /N o (dB) BER MC-CDMA MIMO MC-CDMA (2 x 2) MIMO MC-CDMA (4 x 4) MIMO MC-CDMA with MMSE (2 x 2) MIMO MC-CDMA with MMSE (4 x 4)
- 56. 33 Fig.2.8 Performance of the system with/without MMSE (BPSK, 64 SCs) Fig.2.9 Performance of the system with/without MMSE (BPSK, 128 SCs) 0 2 4 6 8 10 12 14 16 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MC-CDMA MIMO MC-CDMA (2 x 2) MIMO MC-CDMA w ith MMSE(4 x 4) MIMO MC-CDMA w ith MMSE(2 x 2) MIMO MC-CDMA w ith MMSE(4 x 4) 0 2 4 6 8 10 12 14 16 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MC-CDMA MIMO MC-CDMA (2 x 2) MIMO MC-CDMA (4 x 4) MIMO MC-CDMA with MMSE (2 x 2) MIMO MC-CDMA with MMSE (4 x 4)
- 57. 34 Table 2.2 Impact of MMSE Equalization (BPSK) at target BER of 10-3 Antenna con. 16 SC 64 SC 128 SC without Equalization Eb/No (dB) MMSE Equalization Eb/No (dB) without Equalization Eb/No (dB) MMSE Equalization Eb/No (dB) without Equalization Eb/No (dB) MMSE Equalization Eb/No (dB) 2 x 2 14.8 12 14 11.6 13.8 11 3 x 3 14 11 13.5 10.2 12.8 10 4 x 4 11 9 10.3 8 9.8 7.5 Figs.2.8 and 2.9 show the BER performance of the system using 64 and 128 SCs respectively. Comparing Figs. 2.7, 2.8 and 2.9, it can be inferred that the increase in number of SCs improve the performance due to the frequency diversity offered by the SCs. The impact of frequency and spatial diversity is depicted in the Table 2.2. From the Table it is observed that for 16 SCs with 2x2 antenna configuration the Energy per bits to spectral noise density (Eb/No) requirement without equalization is 14.8 dB which is reduced to 14 dB and 13.8 dB for 64 SCs and 128 SCs respectively. Similar behavior is also observed in 3x3 and 4x4 antenna configuration for the target BER of 10-3 . Further the performance is enhanced by equalization technique incorporated in the receiver, for example, Eb/No of 16 SCs (4x4) with equalization is reduced by 2.8 dB than the system without equalization due to reduction of ISI. The reduction of Eb/No is also observed in 64 SCs and 128 SCs i.e 2.4 dB for 64 SCs and 2.8 dB for 128 SCs. The performance of the system is also tested with different antenna configuration such as 2x2, 3x3 and 4x4. For 2x2(16SCs) antenna configuration the Eb/No is 12 dB which is reduced to 11dB for 3x3 and 9 dB for 4x4 antenna configuration. Comparing 2x2 with 4x4 antenna configuration the Eb/No is 3 dB less at target BER of 10-3 .
- 58. 35 2.4.2 Performance of the System with MMSE Equalization (QPSK) The performance of the MIMO MC CDMA system is tested using QPSK modulation and the numerical result is shown in the Table 2.3 when comparing Figs. 2.7, 2.8 and 2.9 with Figs 2.10, 2.11 and 2.12 the latter figures shows slightly lesser performance because of higher order modulation. From the Table 2.3 it is observed that for 16 SCs (3x3) with equalization the Eb/No is 12.8 dB which requires 2.6 dB less Eb/No than the system without equalization. For 64 SCs and 128 SCs too the Eb/No is 2.9 dB and 2.5 dB lesser respectively. The improved performance can be obtained through different antenna diversity. For example in 2x2 (128 SCs) the Eb/No requirement with equalization is 12 dB which is reduced to 11.3 dB for 3x3 and 9.8 dB for 4x4 antenna configuration. The same improvement is also obtained for 16 SCs and 64 SCs. From the above simulation results and discussion, it is clear that the BER of the MIMO MC CDMA using MMSE for BPSK and QPSK modulation is considerably reduced than the system without equalization due to reduction of ISI in the receiver. Fig.2.10 Performance of the system with/without MMSE (QPSK, 16 SCs) 0 2 4 6 8 10 12 14 16 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MC-CDMA MIMO MC-CDMA (2 x 2) MIMO MC-CDMA (4 x 4) MIMO MC-CDMA with MMSE (2 x 2) MIMO MC-CDMA with MMSE (4 x 4)
- 59. 36 Fig.2.11 Performance of the system with/without MMSE (QPSK, 64 SCs) Fig.2.12 Performance of the system with/without MMSE (QPSK, 128 SCs) 0 2 4 6 8 10 12 14 16 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MC-CDMA MIMO MC-CDMA (2 x 2) MIMO MC-CDMA (4 x 4) MIMO MC-CDMA with MMSE (2 x 2) MIMO MC-CDMA with MMSE (4 x 4) 0 2 4 6 8 10 12 14 16 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MC-CDMA MIMO MC-CDMA (2 x 2 ) MIMO MC-CDMA (4 x 4) MIMO MC-CDMA with MMSE (2 x 2) MIMO MC-CDMA with MMSE (4 x 4)
- 60. 37 Table 2.3. Impact of MMSE Equalization (QPSK) at target BER of 10-3 Antenna configuration 16 SCs 64 SCs 128 SCs without Equalization Eb/No (dB) MMSE Equalization Eb/No (dB) without Equalization Eb/No (dB) MMSE Equalization Eb/No (dB) without Equalization Eb/No (dB MMSE Equalization Eb/No (dB) 2 x 2 16 12.8 15 12.5 14.5 12 3 x 3 14.8 12.2 14.5 11.6 13.8 11.3 4 x 4 13 10.5 12.5 10.2 12 9.8 2.5 SUMMARY In this chapter MMSE equalization with ICE is proposed for MIMO MC CDMA system in frequency selective fading channels and tested. Simulation is carried out for MC CDMA system, MIMO MC CDMA system without equalization and MIMO MC CDMA system with MMSE equalization. The proposed system shows improved performance compared to other existing technique due to the restoration of orthogonality and reduction of ISI through MMSE equalization. It is inferred that the system performance can further be improved by increasing the frequency and spatial diversity through the increase in the number of SCs and the antennas. From the simulation results it is concluded that the performance of MIMO MC CDMA system with MMSE equalization increases by 23.45% and 18.3% for BPSK and QPSK respectively compared to the system without equalization.
- 61. 38 CHAPTER 3 PERFORMANCE OF MIMO MC CDMA SYSTEM WITH MPCE BASED MMSE EQUALIZATION 3.1 INTRODUCTION In wireless system, transmitted information reaches the receiver through a radio channel. A channel can describe everything from the source to the sink of a radio signal which includes the physical medium (free space, fiber, waveguides etc.) between the transmitter and the receiver through which the signal propagates. The signals from the transmitter is usually protected by the channel coding and interleaved against fading phenomenon after which the binary signal is modulated and transmitted over multipath fading channel. Due to the multipath channel signals are distorted in a variety of ways like AWGN, amplitude, frequency and phase distortion, resulting in ISI. In order to recover the original transmitted signal at the receiver, it is essential to be aware of channel characteristics like reflection, refraction and diffraction to ensure successful equalization and is possible with the help of channel estimation. The general channel estimation procedure is shown in Fig 3.1. Channel estimation is defined as the process of characterizing the effect of physical channel on the input sequence. Channel estimation can be avoided by using differential modulation technique, however such system result in low data rate and there is a penalty of 3-4 dB less SNR [180-184]. In some cases channel estimation at the user side can be avoided if the base station performs the channel estimation and sends a pre distorted signal [185]. However, for fast varying channels, the pre distorted signal might not bear the current channel distortion, causing system degradation. Hence, system with channel estimation is needed for the future high data rate system.
- 62. 39 The CSI can be obtained through training based, blind and semi blind channel estimation techniques. The blind channel estimation is carried out by evaluating the statistical information of the channel and certain properties of the transmitted signals [186]. It has no overhead but is applicable only to the slow fading channel due to its need for a long data record. In training based channel estimation algorithm, training symbols or pilot tones that are known prior to the receiver are multiplexed along with the data stream for channel estimation [187]. Semi blind channel technique is a combination of blind and training technique utilizing pilots and other natural constraints to perform channel estimation. Fig.3.1 Channel estimation procedure 3.2 TYPES OF CHANNEL ESTIMATION This section discusses in detail the different channel estimation techniques. 3.2.1 Pilot Based Channel Estimation The most preferred and used method to estimate the channel and the offset in frequency is to use pilot symbols. Pilot symbols are known to the transmitter and receiver in advance. The basic idea with pilot symbols is that there is a strong correlation between the pilot symbol fading and the fading of data symbols that are sent Channel Estimated channel model Channel Estimation Technique Transmitted Sequence x(n) Estimated signal 𝑌(n) Error Signal e(n) Actual Received signal Y(n)
- 63. 40 close to the pilot symbol in time and SC. Different types of pilot arrangements are discussed in literature such as block type pilot, group type pilot and irregular pilot distribution [188-191]. In block type pilot based channel estimation shown in Fig.3.2 (a), channel estimation symbols are transmitted periodically, in which all SCs are used as pilots. If the channel is constant during the block, there will be no channel estimation error since the pilots are sent in all carriers. In comb type pilot based channel estimation shown in Fig.3.2 (b), an efficient interpolation technique is necessary in order to estimate channel at data SC by using the channel information at pilot SCs. In Fig.3.2 (a) and 3.2 (b) the pilots are arranged in time and frequency domain respectively. In order to increase the channel capacity irregular pilot arrangements [2, 192] discussed by authors is shown in the Fig. 3.2 (c). Fig.3.2 (a) Block type pilot arrangement Fig.3.2 (b) Comb type pilot arrangement Time F r e q e n c y F r e q e n c y Time
- 64. 41 Fig.3.2 (c) Irregular pilot arrangement 3.2.2 Blind Channel Estimation In pilot or training based channel estimation the transmitter sends a signal known prior to the receiver and is used for estimating the channel condition. However, most wireless devices will be battery powered and the transmission of training signals will seriously affect the robustness of such devices. Moreover, training increases the overhead of the transmitted signal, reducing the net data transmission rate. Thus, it is sensible to use blind channel estimation method that possibly reduces the amount of training required significantly. Typically some special property of the transmitted signal is exploited for blind channel estimation. Blind equalization method provides attractive solution since they do not require any known transmitted data for channel estimation and equalization purposes [193-196] instead they use statistical and structural properties of the communication signals. Channel identification or equalization requires information about both the channel amplitude and phase responses that can be acquired from the received signal. The higher order statistical properties gives phase information however higher order statistics and spectra may not provide a feasible approach for constructing practical equalizers. They have a large variance and consequently large sample sets are needed in order to obtain reliable channel estimates. Even though this is a drawback, can be used, in fast fading channel with high data rates and low computational complexity requirement. F r e q e n c y Time
- 65. 42 3.2.3 Semi Blind Channel Estimation Semi blind technique uses both pilot and natural constraints to estimate the channel. These methods use pilots to obtain an initial channel estimate and improve it by using a variety of prior information. Thus, in addition to the pilots, semi blind methods use the cyclic prefix [197-199], time and frequency correlation [200], Gaussian assumption on transmitted data [201] and virtual carriers [202] for channel estimation and subsequent data detection. 3.3 CHANNEL ESTIMATION FOR OFDM SYSTEM There are two main problems in designing channel estimators for wireless OFDM system. The first problem is the arrangement of pilot information and the second problem is the design of an estimator with both low complexity and good channel tracking ability. In general, fading channel of the OFDM system can be viewed as a two dimensional (2D) signal (time and frequency). The optimal channel estimator in terms of mean square error is based on 2D Wiener filter interpolation. Unfortunately, such a 2D estimator structure is too complex for practical implementation due to need of power delay profile and as Doppler spread is not known in wireless environment[252-254]. The combination of high data rate and low bit error rate in OFDM system necessitates the use of estimators that have both low complexity and high accuracy, where the two constraints work against each other and a good tradeoff is needed. The one dimensional (1D) channel estimations are usually adopted in OFDM system to accomplish the tradeoff between complexity and accuracy [203-209]. The two basic 1D channel estimations are block type pilot channel estimation and comb type pilot channel estimation in which the pilots are inserted in the frequency and in the time domain as shown in Fig.3.3. The estimations for the block type pilot arrangement can be based on least square (LS), MMSE and modified MMSE. The estimations for the comb type pilot arrangement includes the LS estimator with 1D interpolation, the maximum likelihood (ML) estimator, and the parametric channel modeling based estimator. Other channel estimation such as
- 66. 43 estimators based on simplified 2D interpolations, the estimators based on iterative filtering and decoding, estimators for the OFDM system with multiple transmit and receive antennas are studied in [210-213]. Fig.3.3 Block diagram of OFDM transceiver with pilot based channel estimation 3.4 PILOT BASED CHANNEL ESTIMATION FOR MC CDMA SYSTEM The block diagram of MC CDMA system incorporating channel estimation and equalization is shown in Figs.3.4 and 3.5. In the transmitter after modulation the data stream is spread by the wideband PN sequence to form the chip level transmit matrix. The pilot signals are added for estimating channel response in the receiver and modulated by the IFFT. Subsequently the output samples of the IFFT are parallel to serial (P/S) converted to form the baseband signal which is added with cyclic prefix (CP). Upon receiving the signal, CP is removed and FFT is performed and the pilot symbols are used for obtaining transfer function of the channel which in turn is used for reducing the amplitude and phase distortion using equalization. With the knowledge of estimated channel, data symbols are recovered after despreading the signal with help of PN sequence. AWGN Data Out Channel Estimation Mapping Serial to parallel Pilot insert IFFT Add Cyclic Prefix Parallel to serial Channel Demap Serial to Parallel Remove Cyclic Prefix FFT Pilot Extract Parallel to Serial Data In
- 67. 44 Fig.3.4 Block diagram of MC CDMA transmitter with pilot signal Fig.3.5 Block diagram of MC CDMA receiver with pilot based channel estimation 3.5 MODIFIED PILOT CHANNEL ESTIMATION (MPCE) BASED MMSE EQUALIZATION FOR MIMO MC CDMA SYSTEM From the literature survey it is observed that different types of equalizations are discussed for MIMO, OFDM and MC CDMA without channel estimation to reduce ISI. Equalization can be effected in a better way if the channel characteristics are estimated a priori through estimation techniques. Pilot based channel estimation is proved to be effective with less estimation error and the performance is decided basically on the number and location of pilots. In this work, MPCE technique is proposed and incorporated, in which, the location and number of pilots in time and frequency domain is dynamically varied with the help of channel estimation itself. Utmost care is taken to ensure that the pilots are Nyquist rated all the time based on the frequency selectiveness and fastness of the fading. Also, delay spread and Doppler spread threshold(Th) is decided based on the traffic and target BER required. This modification shows a clear improvement in performance and is observed through simulation. A simple flow chart of proposed MPCE is depicted in Fig.3.6 (a) and (b). MappingData CP Insertion IFFT P/S PN Sequence PilotS/P FFT PN Sequence Detector CP Removal P/S Equalization Estimated Data S/P Channel Estimation
- 68. 45 The equalization is modified to adjust the amplitude and phase distortion with the help of CSI obtained from MPCE technique. Fig.3.7 and 3.8 shows the proposed model of MIMO MC CDMA transmitter and receiver respectively with the modified pilot channel estimation and equalization. The transmitter of MIMO MC CDMA consists of BPSK/QPSK modulator, PN sequence spreader and OFDM modulator. After modulating, the data stream is multiplied by a spreading sequence and the length of this spreading code is usually identical to the number of SC. The pilot signals are first multiplexed to the data streams then OFDM modulation is performed which is transmitted through multiple antennas. Fig. 3.6 (a) Flow chart of MPCE (Delay spread). (b) MPCE (Doppler spread) NoYes Yes Decrease number of pilot Delay spread =Th CSI Increase number of pilot Delay spread >Th No NoYes Yes Decrease number of pilot Doppler spread =Th CSI Increase number of pilot Doppler spread >Th No
- 69. 46 Fig.3.7 Block diagram of modified pilot insertion based transmitter Fig.3.8 Block diagram of MPCE based MMSE equalization PN Sequence FFTDetector CP Removal P/S MMSE Equalization Estimated Data S/P MPCE MappingData PN Sequence Pilot insertion based on MPCE S/P CP Insertion IFFT P/S 46
- 70. 47 In the receiver the signals are received through multiple antennas and demodulated using FFT. After OFDM demodulation the user data symbols and pilot symbols are recovered by despreading with corresponding spreading codes. The required transfer function for channel estimation and equalization is recovered from pilot sequence. Finally the original data stream is recovered by dividing the received signal by channel response. At the receiver end, the demodulator processes the channel equalized waveform and reduces each waveform to a scalar (or) a vector that represents an estimation of the transmitted data symbol. The detector, which follows the demodulator, decides whether the transmitted bit is 0 or 1. The received signal after FFT is given by ( ) ( ) ( ) ( ), 0,1,.., 1cY k X k H k W k k N (3.1) The pilot channel estimation procedure is shown in Fig.3.9, where the received pilot signals YP(k) are extracted from Y(k), the channel transfer function 𝐻(k) can be obtained from the information carried by 𝐻P(k) with the knowledge of the channel responses H(k), the transmitted data samples X(k) can be recovered by simply dividing the received signal by sample response. Finally the signals are equalized by MPCE based MMSE technique. Fig.3.9 MPCE estimation procedure Pilot signal Extraction Pilot SC Estimation Channel Interpolation Received Signal after FFT Estimated Channel Response… … … … Known pilots XP(k) Y(k) YP(k) 𝐻P(k) 𝐻(k)
- 71. 48 3.6 SIMULATION RESULTS AND DISCUSSION MIMO MC CDMA system with diversity technique is simulated using MATLAB with the parameters given in Table 3.1. The result shows the BER performance with respect to Eb/No of the system with/ without MPCE based MMSE equalization under Rayleigh fading channel. The diversity technique uses 2, 3 and 4 antennas for both transmitter and receiver. The result indicates that with diversity, performance of the system improves in terms of BER. Along with the diversity technique the system was tested with different SCs (16, 64, and 128) and different modulation techniques (BPSK, QPSK & 16QAM). From the result it is observed that when the number of SCs is increased, the performance of the system gets increased due to reduction of ISI. As the number of SCs increases, the frequency diversity also increases which in turn reduces the error rate. Table 3.1 Simulation parameters of the system with MPCE based MMSE equalization Spreading Codes Walsh Hadamard Code Number of SCs 16/64 /128 Channel Rayleigh fading Modulation BPSK/QPSK/16 QAM Antennas 2x2/ 3x3/ 4x4/2x3/3x2 Estimation/Equalization technique MPCE/MMSE
- 72. 49 3.6.1 Performance of the System with MPCE Based MMSE Equalization (BPSK) Fig.3.10 shows the BER performance of MIMO MC CDMA with/without MPCE based MMSE equalization for BPSK modulation with different antenna configuration. From the graphs it is evident that the system with MMSE equalization and pilot based channel estimation or MPCE performs better due to the reduction of ISI. The same system is tested for different SCs also, which is shown in Fig. 3.11 and 3.12 and it is observed that the performance of the system gradually increases due to the increase in number of SCs i.e as frequency diversity increases the ISI reduces by reducing the depth of the fading. The detailed report of the simulation result is shown in the Table 3.2. For 16 SCs (4x4) the Eb/No requirement is 9.7 dB at the target BER of 10-3 which is reduced to 9.2 dB for 64 SCs and 8.4 dB for 128 SCs. Fig.3.10 Performance of the system with MPCE based MMSE (BPSK, 16 SCs) 0 5 10 15 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA (2 x 2) MIMO MC-CDMA (4 x 4) MIMO MC-CDMA (2 x 2) with MMSE & MPCE MIMO MC-CDMA (4 x 4) with MMSE & MPCE
- 73. 50 Fig.3.11 Performance of the system with MPCE based MMSE (BPSK, 64 SCs) Fig. 3.12 Performance of the system with MPCE based MMSE (BPSK, 128 SCs) 0 5 10 15 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA(2 x2 ) MIMO MC-CDMA(4 x4 ) MIMO MC-CDMAwith MMSE & MPCE (2 x2 ) MIMO MC-CDMAwith MMSE & MPCE (4 x4 ) 0 5 10 15 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA (2 x 2 ) MIMO MC-CDMA (4 x 4 ) MIMO MC-CDMA with MMSE & MPCE (2 x 2 ) MIMO MC-CDMA with MMSE & MPCE (4 x 4 )
- 74. 51 Table 3.2 Impact of MPCE based MMSE equalization (BPSK) at target BER of 10-3 Antenna confi. 16 SCs 64 SCs 128 SCs without Equalization Eb/No (dB) MPCE based MMSE Eb/No (dB) without Equalization Eb/No (dB) MPCE based MMSE Eb/No (dB) without Equalization Eb/No (dB) MPCE based MMSE Eb/No (dB) 2 x 2 14.8 13.2 14 12.2 13.8 11.8 3 x 3 14 12.5 13.5 11.2 12.8 10.6 4 x 4 11 9.7 10.3 9.2 9.8 8.4 It is also noticed that when the number of antennas are increased, the performance of the system is increased due to the spatial diversity. For example in Table 3.2 ,the Eb/No of 128 SCs (2x2) is 11.8 dB and it is reduced to 10.6 dB for 3x3 antenna configuration and 8.4 dB for 4x4 antenna configuration, moreover the similar behavior is also observed in 64 SCs and 16 SCs. From the above discussion it is clear that the performance of the system is improved by increasing the antenna configuration and SCs. However the system performance of MIMO MC CDMA system with MPCE based MMSE equalization is less compared with ICE discussed in chapter 2, however the ICE is practically not possible. 3.6.2 Performance of the System with MPCE Based MMSE Equalization (QPSK) The performance of the proposed system is tested for QPSK modulation and the simulation result is shown in Figs. 3.13, 3.14 and 3.15 Table 3.3. Comparing Figs. 3.10, 3.11, 3.12 and Table 3.2 with Figs 3.13, 3.14, 3.15 and Table 3.3, it is evident that the system with QPSK has lesser performance due to the higher order modulation used. However in this case also performance improves due to increase in SC and antenna configuration.
- 75. 52 Fig.3.13 Performance of the system with MPCE based MMSE (QPSK, 16 SCs) Fig.3.14 Performance of the system with MPCE based MMSE (QPSK, 64 SCs) 0 5 10 15 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA (2 x 2 ) MIMO MC-CDMA (2 x 2 ) MIMO MC-CDMA with MMSE & MPCE (2 x 2 ) MIMO MC-CDMA with MMSE & MPCE (4 x 4 ) 0 5 10 15 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA (2 x 2 ) MIMO MC-CDMA (4 x 4 ) MIMO MC-CDMA w ith MMSE& MPCE(2 x 2 ) MIMO MC-CDMA w ith MMSE& MPCE(4 x 4 )
- 76. 53 Fig. 3.15 Performance of the system with MPCE based MMSE (QPSK, 128 SCs) Table 3.3 Impact of MPCE based MMSE equalization (QPSK) at target BER of 10-3 Antenna confi 16 SCs 64 SCs 128 SCs without Equalization Eb/No (dB) MPCE based MMSE Eb/No (dB) without Equalization Eb/No (dB) MPCE based MMSE Eb/No (dB) without Equalization Eb/No (dB) MPCE based MMSE Eb/No (dB) 2 x 2 16 14 15 13.2 14.5 12.5 3 x 3 14.8 13.2 14.5 12.1 13.8 11.8 4 x 4 13 11.8 12.5 10.4 12 10 For example in Table 3.3 the Eb/No requirement for 16 SCs (3x3) antenna configuration is 13.2 dB and it is decreased to 12.1 dB for 64 SCs and 11.8 dB for 128 SCs. It is also observed that for 2x2 antenna configuration (64 SCs) the Eb/No requirement is 13.2 dB and it is reduced to 12.1 dB for 3x3 and 10.4 dB for 4x4 antenna configuration. 0 5 10 15 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA (2 x 2 ) MIMO MC-CDMA (4 x 4 ) MIMO MC-CDMA with MMSE & MPCE (2 x 2 ) MIMO MC-CDMA with MMSE & MPCE (4 x 4 )
- 77. 54 3.6.3 Performance of the System with MPCE Based MMSE Equalization (16 QAM) The performance of the MIMO MC CDMA system is tested for 16 QAM modulation for different SCs and antenna configuration and the numerical result is shown in Fig.3.16 to 3.21 and Table 3.4. In this case also the performance is improved by increasing the antenna configuration and SCs but the performance of the system with 16 QAM is less than the system with BPSK/QPSK due to higher order modulation. From the Table it is observed that the Eb/No requirement for 16 SCs (2x2) with equalization at the target BER of 10-3 is 18.2 dB, which is reduced to 17.6 dB and 15 dB for 64 SCs and 128 SCs respectively. Similarly the Eb/No requirement for 2x2 antenna configuration with equalization (128 SCs) is 15dB and it is reduced to 12 dB by increasing antenna configuration as 4x4. The same is observed for 16 SCs and 64 SCs also. Fig.3.16 Performance of the system with MPCE based MMSE (2x2, 16 SCs, 16 QAM) 0 5 10 15 20 25 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA MIMO MC-CDMA with MMSE & MPCE
- 78. 55 Fig.3.17 Performance of the system with MPCE based MMSE (2x2, 64 SCs, 16 QAM) Fig.3.18 Performance of the system with MPCE based MMSE (2x2, 128 SCs, 16 QAM) 0 5 10 15 20 25 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA MIMO MC-CDMA with MMSE & MPCE 0 5 10 15 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA MIMO MC-CDMA with MMSE & MPCE
- 79. 56 Fig.3.19 Performance of the system with MPCE based MMSE (4x4, 16 SCs, 16 QAM) Fig.3.20 Performance of the system with MPCE based MMSE (4x4, 64 SCs, 16 QAM) 0 5 10 15 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA MIMO MC-CDMA w ith MMSE& MPCE 0 5 10 15 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA MIMO MC-CDMA with MMSE & MPCE
- 80. 57 Fig.3.21 Performance of the system with MPCE based MMSE (4x4, 128 SCs, 16 QAM) Table 3.4 Impact of MPCE based MMSE equalization (16QAM) at target BER of 10-3 Antenna confi 16 SCs 64 SCs 128 SCs without Equalization Eb/No (dB) MPCE based MMSE Eb/No (dB) without Equalization Eb/No (dB) MPCE based MMSE Eb/No (dB) without Equalization Eb/No (dB) MPCE based MMSE Eb/No (dB) 2 x 2 18.8 18.2 18.1 17.6 16 15 4 x 4 16.7 15 15.2 13.5 14.2 12 0 5 10 15 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC-CDMA MIMO MC-CDMA with MMSE & MPCE
- 81. 58 3.7 SUMMARY In this chapter MPCE based equalization technique is proposed for MIMO MC CDMA system to effectively combat the ISI and ensure target BER for different service. As the MMSE equalization with ICE is not practical (described in chapter 2), to analyze in the realistic situation the simulation is repeated with MPCE based MMSE equalization. From the simulation result the equalization with MPCE shows lesser performance when compared to equalization with ICE. The proposed system is tested for different SCs, antenna configuration and Modulation technique. It is also inferred that increase in the number of transmitting and receiving antennas and increase in the number of SCs improves the performance due to the spatial and frequency diversity respectively. From the numerical result it is observed that for 128 SCs (4x4) MIMO MC CDMA system with equalization based on MPCE the Eb/No is required 2 dB less when compared to MIMO MC CDMA without equalization for QPSK and 2.2 dB less Eb/No for 16 QAM modulation. It should also be noted, with ICE based equalization the Eb/No requirement is 9.8 dB for QPSK and 11.8 dB for 16 QAM which is lesser than estimation based on MPCE but ICE cannot be practically realized.
- 82. 59 CHAPTER 4 PERFORMANCE OF MIMO MC CDMA SYSTEM WITH MPCE BASED MMSE EQUALIZATION AND ITBF 4.1 INTRODUCTION In mobile environment the desired signal and the interference occupy the same frequency band. Unless the signals have low correlation (like in CDMA systems) temporal processing will not be effective in interference and noise rejection. However the desired signal and interference arrive from different direction and hence have different arrival time and angles of arrival. BF techniques are designed to transmit or receive the signal to/from the preferred direction, where multiple antennas are used to shape the overall antenna beam in a certain way to maximize antenna gain in target direction and to suppress target dominant interference. The main goal of BF is to increase received SNR and subsequently to improve coverage. The useful signal in a targeted direction is enhanced by constructive combination, whereas noise or interference are rejected by destructive combination. For reducing ISI, channel estimation based equalization is a corrective measure incorporated in the receiver as discussed in chapter 3. To reduce the influence of ISI further and to improve the quality of received signal it is possible to use BF technique as a preventive measure [249, 250]. The BF can be implemented both in transmitter and receiver through multiple antennas [110,214]. For the receiver BF case, the received signal can be optimally combined from different receiver antennas in order to maximize the received SINR. The mean received SINR increases proportional to the number of receiving antenna (Nd) and array gain by 10 log10(Nd) dB over the single antenna.
- 83. 60 For the transmit BF case CSI is required and can be obtained from feedback [92,95,215,216]. The transmit BF is based on perfect channel information that can be designed to maximize the received SINR. The average received SINR increases proportionally to the number of transmitting antenna(Ns) and array gain by 10 log10(Ns) dB over the single antenna. Transmit BF has attracted much attention recently. By using partial or full knowledge of the CSI at the transmitter, transmit BF can greatly enhance energy efficiency in wireless communications. On the receive antenna, the received signal is, 1 2 1 2( ) x y h h n h h x n x (4.1) where y, is the received signal, hi are the channel coefficients seen from the ith transmit antenna, x is the transmitted symbol and n is the noise at the receiver antenna. When transmit BF is applied, the symbol from each transmit antenna multiply with a complex number corresponding to the inverse of the phase of the channel so as to ensure that the signals add constructively at the receiver. The received signal can be expressed as 1 2 1 2 j j e y h h x n e (4.2) and the channel coefficients are further expressed as: 1 1 1 j h e h (4.3) 2 2 2 j h e h (4.4) For applying BF to the simple system, set two BF parameters 1j e and 2j e the beam formed signal arrived at the receiver is modulus (to avoid phase difference) of channel co-efficient and is expressed as 1 2( )y h h x n . It is to be noted that the effective channel coefficients add in phase, so that the effect of multiple paths is used to an advantage, ultimately improving the strength of the received signal. 4.2 TYPES OF BEAMFORMING
- 84. 61 Beamforming can be classified as fixed beamforming, switched beamforming, delay and sum beamforming, beam space beamforming and adaptive beamforming. 4.2.1 Fixed Beamforming In fixed BF the interference is not completely suppressed and also it does not perform amplitude weighting of the received signal which can be realized by adopting either an analog approach (e.g. switched beam, delay and sum) or a digital approach (e.g. beam space BF) where the system can be usually realized at a reasonable cost. Switched Beamforming In switched beam antennas the performance of the system can be improved by multiple fixed beams which generate a finite number of radiation patterns, thus making one RF signal available for each possible beam as shown in Fig.4.1. The patterns are synthesized using an RF network that combines the signals received by the N antennas in order to form the different beams targeted towards certain directions. The RF network (Butler matrix) is realized adopting analog phase shifters [217-219]. An RF switch selects the signal with the highest SINR from the set of available outputs, which will be further processed by the Low Noise Amplifier. Fig. 4.1 Switched Beamforming Delay and sum beamforming Butler Matrix RF Switch
- 85. 62 The most ancient form of spatial filtering that adopts an analog approach is the delay and sum BF, where delays are used instead of phase shifters as shown in Fig. 4.2 [220]. For narrowband signals this scheme is substantially equivalent to switched beam but the delay and sum technique can also be applied to broadband signals. Depending on the delays, the antenna array isolates the signal for certain directions but the interference is not specifically taken into account because this scheme does not perform any amplitude weighting. Fig.4.2 Delay and sum Beamforming Beam space beamforming The two previous methods are examples of analog fixed BF. Beam space, instead, is a fixed BF technique that adopts a digital approach. In Fig 4.3 rather than directly weighting the signals incoming on the array elements the antenna outputs can be first processed in order to produce a set of orthogonal multiple beams. Fig.4.3 Beam space Beamforming 4.2.2 Adaptive Beamforming Output SUM Z1 Zn SUM DC & ADC DC & ADC Beam Selection Beam Selection X1 Xn 𝑥1 𝑥 𝑛
- 86. 63 Adaptive Antennas are also referred to as smart antenna systems and represents advanced techniques able to maximize the SINR at the array output. They find application in environments where the spatial correlation between the signal replicas is high. The radiation pattern is dynamically controlled to perform the electrical beam steering to a desired direction, and null steering to reject interfering signals. In mobile radio environment, the users keep moving and hence they are time varying. Also the parameters of the user signals vary in time due to the presence of multipath, cochannel interference and noise associated with the channel. Fixed weights will not track these changes in the time varying channel. An adaptive antenna array can change its beam pattern in response to the changing signals. This kind of antenna system usually works with some internal feedback whereby the system can modify the antenna patterns. The weights used must be changed using adaptive algorithms. Such algorithms are usually designed to meet prime performance criteria‟s like MSE, ML, Maximum SNR and Maximum SINR, generating a set of equations. These performance criteria are usually expressed as cost functions and the weights are adapted iteratively until the cost function converges to a minimum value. 4.3 MIMO MC CDMA SYSTEM WITH MPCE BASED MMSE EQUALIZATION AND ITBF Even though MPCE based equalization is employed in the receiver to tackle the effect of multipath and reduce ISI, it is better to beamform the signals from the transmitter antennas based on CSI to further improve the quality of the received signal. In this work, improved transmit BF (ITBF) technique is proposed, which utilize the CSI details obtained from the modified channel estimation algorithms discussed in Chapter 3. In the proposed ITBF technique, BF is dynamically carried out i.e. the rate at which the BF vectors are updated matches with the rate at which channel varies. Hence it is ensured, the moment there is a change in CSI, change in BF vector is invoked. The logic used in ITBF is depicted in the flowchart is shown in Fig.4.4. No Yes ITBF vectors CSI = Th
- 87. 64 Fig.4.4 Flowchart of ITBF Fig 4.5 shows the block diagram of MIMO MC CDMA system with MPCE based equalization and ITBF. In the transmitter after modulation and spreading, the pilot signals are added for estimating channel response in the receiver and OFDM modulated by IFFT. The signals are beam formed through ITBF technique and transmitted to the destination through multiple antennas. Upon receiving the signal the channel condition is identified using pilot signal and again fed back to the transmitter to improve the performance of the system.
- 88. 65 Fig 4.5 Block diagram of MIMO MC CDMA system with MPCE based MMSE and ITBF CP Insertion IFFT P/S ITBF Unit Pilot insertion based on MPCE PN Sequence MappingData S/P PN Sequence MMSE Equalization Detector CP Removal FFTP/S Estimated Data S/P MPCE 65
- 89. 66 4.4 SIMULATION RESULTS AND DISCUSSION The MIMO MC CDMA system with MPCE based MMSE equalization and ITBF is simulated for different diversity techniques using MATLAB with the parameters given in Table 4.1. The result shows the BER performance with respect to Eb/No for MPCE based equalization and ITBF under Rayleigh fading channel. Performance of the system improves in terms of BER due to the antenna diversity. The MIMO MC CDMA system is tested with different SC (16, 64, and 128) for QPSK/16 QAM modulation and 2x2/4x4 antennas. Table 4.1 Simulation parameters of the system with MPCE based MMSE and ITBF Spreading Codes Walsh Hadamard Code Number of SCs 16/ 64 /128 Channel Rayleigh fading Modulation QPSK/16QAM Antennas 2x2/4x4 Equalization/Estimation MMSE/MPCE 4.4.1 Performance of the System with MPCE Based MMSE and ITBF (QPSK) Fig.4.6 and Table 4.2 shows the BER performance of MIMO MC CDMA,MIMO MC CDMA with MMSE, MIMO MC CDMA with MPCE and ITBF for QPSK modulation with 4 x 4 antenna configuration. From the result it is evident that the system with MPCE and ITBF performs better than the system with MPCE alone. This is the actual contribution of ITBF which ensures, at every instant, BF is invoked dynamically to tackle the impairments of channel. From the Table 4.2. it is observed that the Eb/No requirement for MIMO MC CDMA system with MPCE and ITBF is 2.8 dB less compared with the system using MPCE alone for the target BER of 10-3 due to emphasis of the signal in the direction of desired user and reduction of the interference from other user.
- 90. 67 Fig.4.6 Performance of 4 x 4 antennas configuration (128 SCs, QPSK) Table 4.2 Impact of MPCE based MMSE and ITBF at target BER of 10-3 (QPSK) MIMO MC CDMA system Eb/No (dB) 12 System with MPCE based MMSE equalization Eb/No (dB) 10 System with MPCE based MMSE equalization and ITBF Eb/No (dB) 7.2 4.4.2 Performance of the system with MPCE based MMSE and ITBF (16 QAM) The same system is tested for different SCs and antenna configuration for 16 QAM, which is shown in the Figs. 4.7 to 4.12 and Table 4.3. From the results, it is observed that the performance of the system gradually increases due to the frequency diversity offered by the increase in SCs. Fig. 4.10, Fig.4.11 and Fig.4.12 shows the BER performance of MIMO MC CDMA for 4x4 antenna configuration. 0 5 10 15 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No BER MIMO MC CDMA MIMO MC CDMA with MPCE based MMSE MIMO MC CDMA with MPCE based MMSE & ITBF
- 91. 68 Fig.4.7 Performance of 2 x 2 antennas configuration (16 SCs, 16 QAM) Fig.4.8 Performance of 2 x 2 antennas configuration (64 SCs, 16 QAM) 0 5 10 15 20 25 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA w ith MPCEbased MMSE MIMO MC CDMA w ith MPCEbased MMSE& ITBF 0 5 10 15 20 25 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA w ith MPCEbased MMSE MIMO MC CDMA w ith MPCEbased MMSE& ITBF
- 92. 69 Fig.4.9 Performance of 2 x 2 antennas configuration (128 SCs, 16 QAM) Fig.4.10 Performance of 4 x 4 antennas configuration (16 SCs, 16 QAM) 0 5 10 15 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA with MPCE based MMSE MIMO MC CDMA with MPCE based MMSE & ITBF 0 5 10 15 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA w ith MPCEbased MMSE MIMO MC CDMA w ith MPCEbased MMSE& ITBF
- 93. 70 Fig.4.11 Performance of 4 x 4 antennas configuration (64 SCs, 16 QAM) Fig.4.12 Performance of 4 x 4 antennas configuration (128 SCs, 16 QAM) 0 5 10 15 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA w ith MPCEbased MMSE MIMO MC CDMA w ith MPCEbased MMSE& ITBF 0 5 10 15 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA with MPCE based MMSE MIMO MC CDMA with MPCE based MMSE & ITBF
- 94. 71 Comparing Fig. 4.7, Fig. 4.8 and Fig.4.9 with Fig. 4.10, Fig. 4.11 and Fig.4.12, the latter figures show improved performance when the number of antenna is increased due to the exploitation of spatial diversity. From the simulation results it is quite clear that in all the cases the system with MPCE based MMSE equalization and ITBF performs better. From the Table 4.3 it is noted that the Eb/No requirement is reduced from 15 dB to 7.8 dB by increasing SCs from 16 to 128 as well as increasing the antenna configuration from 2x2 to 4x4. Table 4.3 Impact of MPCE based MMSE and ITBF at target BER of 10-3 (16 QAM) Ant. confi. 16 SCs 64 SCs 128 SCs MPCE based MMSE equalization Eb/No (dB) MPCE based MMSE with ITBF Eb/No (dB) MPCE based MMSE equalization Eb/No (dB) MPCE based MMSE with ITBF Eb/No (dB) MPCE based MMSE equalization Eb/No (dB) MPCE based MMSE ITBF Eb/No (dB) 2 x 2 18.2 15 17.6 14.2 15 11.8 4 x 4 15 11.7 13.5 10.3 12 7.8 4.5 SUMMARY ITBF is incorporated in MIMO MC CDMA to effectively tackle the channel impairments. ITBF is made dynamic to suit the nature of fast fading channel and from the simulation result it is observed system with ITBF and MPCE based equalization is better than the system with MPCE based equalizer alone. From the numerical result it is observed that the performance of the system increases due to the diversity offered by ITBF in the range of 17.58% to 21.22% for 2x2 antennas and 22% to 35% for 4x4 antenna configuration.
- 95. 72 CHAPTER 5 PERFORMANCE OF MIMO MC CDMA SYSTEM WITH MPCE BASED MMSE EQUALIZATION, ITBF AND NR 5.1 INTRODUCTION Equalization employed in the receiver is a corrective measure to reduce ISI, whereas BF is a preventive measure incorporated in the Transmitter. To further improve the performance, relaying techniques are incorporated in the channel to increase SNR, improve coverage, reduce power requirements at mobile stations and will effectively reduce number of base station. The source (S) transmits to the relay (R) nodes and the signal is retransmitted to the destination (D). When there is a direct link between S and D, the D node will receive two replicas of the same data transmitted through two different and independent channels: the S-R-D channel and the S-D channel. The D node combines the direct signal in the first phase and the relayed signals in the second phase to improve the SNR. Since the R nodes are generally placed in different physical locations, a diversity gain can be realized due to the spatial separation of the relays which would enforce independent fading. Cooperative communication, an alternative technique to provide impressive performance by exploiting spatial diversity in wireless transmission. [231-233], has attracted researchers due to its ability to extend system coverage, enhance spectrum efficiency and reduce the need for physical deployment of more number of base station [250]. The basic idea of cooperative communication is that all mobile users in a wireless network can help each other to send signals to the D cooperatively. Thus, it is inherently more reliable for the destination to receive the transmitted information, since from a statistical point of view, the chance of having all transmission links to the destination failing is seldom.
- 96. 73 5.2 CLASSIFICATION OF RELAY SYSTEM Based on number of relay nodes used in the communication system the relay can be classified as single relay, multiple relay and MIMO multi relay system. Based on transmission the relay can be classified as one way and two way relay system. 5.2.1 Single Relay System The relays can be mobile terminals (user terminals) or fixed terminals which are known as infrastructure relays. When there is only one R node assigned to assist the communication between the S and the D, it is called single relay system as shown in the Fig.5.1 Fig.5.1 Simple three node relay 5.2.2 Multiple Relay System In multiple relay, the S communicates with the D through L relay terminals R1, . . . , RL. All terminals are equipped with one antenna and operate in a half duplex mode. The transmission for every information symbol happens in two phases. In the first phase, the S transmits the signal to the D through the direct channel and to the Rs. In the second phase, the Rs simultaneously forward their received signals to the D. In Fig 5.2(a) due to deep large scale fading effects produced by the long distance, the direct link is not included between the S and the D. If communication through R is not possible, direct link is established between S and D as shown in Fig 5.2(b). S D R
- 97. 74 Fig.5.2(a) Multi relay without direct link to D Fig.5.2(b) Multi relay with direct link to D 5.2.3 MIMO Multi Relay System The combination of relaying and MIMO techniques is advantageous as it increases the data rate in the cellular edge and extends the network coverage. Fig 5.3(a) shows MIMO relay networks which has been extensively investigated in [221-228]. In addition, MIMO multi relay networks also has been studied in [229, 230, 247] and it is shown in the Fig 5.3(b). Fig.5.3(a) MIMO relay with direct link to D D R1 RS RL S D R R R S D R
- 98. 75 Fig.5.3(b) MIMO Multi relay without direct link to D The MIMO multi relay network consists of a single S and D node, both equipped with multi antennas and relay network consisting of parallel relays with multi antennas. Data transfer between S to D takes two non overlapping time slots for the data to be transmitted from the S to the D via the S-R and R-D channels. 5.3 ONE WAY AND TWO WAY RELAY SYSTEM In One way relay system, the information is always transmitted from the S node and the D receives the signal from S. In two way scenario, the communication is bi directional where two users exchange information. Under this scenario, each node is not only a S, but also the intended D of the other node. 5.3.1 One Way Half Duplex In half duplex transmission the data transmission will flow in one direction at a time. Different modules of one way half duplex are as follows: In module I, during the first time slot the S communicates with the R and D (solid lines in Fig. 5.4(a)) and in the second time slot the R communicates with the D (dashed line). This module shows the same structure as the Broadcast Channel (BC) during the relay receive phase. This module is used by the cooperative transmission defined in [221,234] and also in the relay assisted transmission described in [235]. S D R R R
- 99. 76 Fig.5.4(a) One way half duplex module 1 On the other hand, in module II during the first slot the S transmits to the R (solid line in Figure 5.4(b)). It is assumed that the D is unable to receive the message from the S in that phase. In the second time slot S and R transmits simultaneously to the D (dashed lines). Hence in the relay transmit time slot the channel becomes a multiple access channel. Fig.5.4(b) One way half duplex module II Fig.5.4(c) One way half duplex module III Module III is the combination of module I and II. The S transmits to the R and the D (solid lines in Figure 5.4(c)) in the first time slot. Then, in the second time slot, the D and the R transmits to the destination (dashed lines). Notice that the R is transmitting during the second phase, so that it cannot be aware of the signal transmitted by the S in the second phase. This module can achieve a better spectral efficiency than the previous modules but has disadvantage of increased system complexity. S D R S D R S D R
- 100. 77 Fig.5.4(d) One way half duplex module IV( Forwarding) Module IV, the traditional forwarding module consists of a transmission from S to R during the first time slot and a transmission from R to D in the second time slot as in Figure 5.4(d). It should be emphasized that the half duplex relay module discussed previously (module I, II and III) makes good use of the S-D link in contrast to the forwarding module. Likewise, if that link presents a very bad quality compared with the S-R and R-D link, the performance obtained by modules I, II and III converges to the forwarding one. 5.3.2 Two Way Half Duplex Mode The relay scheme used in one way communication system can also be used for two way relay system with some modifications. In a typical one way relay system, the communication is established in two approaches. A straight forward approach is to avoid interference which deploys two successive one way relay schemes. Since most of the wireless devices operate in half duplex transmission mode, a two way relaying scheme requires four phases (in time or frequency) to accomplish the exchange of symbols between the two transceivers. Fig. 5.5 illustrates the two way relay channel which is composed of two S nodes S1, S2 and an R node. The module I is also referred as a traditional transmission scheduling scheme. For this transmission scheme, interference is usually avoided by prohibiting the overlapping of signals in the same time slot. A possible transmission schedule is given in Fig. 5.5(a). In the First time slot, the node S1 transmits the data symbol to the R. In the second time slot the R node simply amplifies and forwards what S D R
- 101. 78 it has received from the first time slot to node S2. This process finishes the signal transmission from node S1 to S2. In the third and fourth time slots, transmission is just in the reverse direction to complete the signal transmission from S2 to S1. A total of four time slots are needed for exchanging of data symbols in opposite directions. Fig. 5.5(a) Two way half duplex module I Fig. 5.5(b) Two way half duplex module II In Module II shown in Fig.5.5 (b), during the First time slot, node S1 transmits data symbols to the R node. In the second time slot S2 transmits another data symbols to the R node. Finally in the third time slot, the R node first combines what it has received from those two terminals, and then forwards a scaled version of this symbol to both nodes S2 and S1. A total of three time slots are needed, for a throughput improvement of 33% over the traditional transmission scheduling scheme. Module III shown in Fig.5.5(c) is the most efficient transmission scheme and in this module only 2 time slots are needed to complete the information exchanging process. In the 1st time slot, both S1 and S2 transmit data symbols to the R node. Then, in the second time slot (the Broadcasting phase), R forwards the received signals to both end terminals to complete this information exchanging process. Comparing these three modules the system transmission methods are the same for S1 S2 R 1st 2nd 4th 3rd S1 S2 R 1st 2nd 3rd 3rd
- 102. 79 every module (two way relay system without direct channel) where the maximum diversity of all the three modules must be the same. Therefore it can be concluded that module III always outperforms module I and module II in terms of diversity multiplexing tradeoff. Fig. 5.5(c) Two way half duplex module III Fig. 5.5(d) Two way half duplex module IV Module IV in Fig.5.5 (d) illustrates the two way relay system which contains the direct link between the two end terminals. For this system, with the same assumptions as the system without direct link, the operation of the module is shown as following. In the first time slot, S1 transmits data symbols to both R and S2. In the second time slot S2 transmits another data symbols to the R and S1. Finally, in the third time slot, R combines the data received from the first two time slots and forwards it to both the end terminals S1 and S2. Module IV has higher spatial diversity than module III but when two end terminals are out of range, direct link is not possible. In order to achieve two way relay transmission in fewer phases, R requires a more complicated hardware and a higher signal processing capability and the received data from the two transceivers need to be combined at R. There are different approaches of combining data such as superposition coding, network coding and Lattice coding. In superposition coding, R retransmits the linear sum of S1 S2 R 1st 1st 2nd 2nd S1 S2 R
- 103. 80 the two sets of symbols containing the decoded data of the two transceivers. Each transceiver subtracts its own data first and then decodes the data of the other user [236]. In network coding, R combines the data from two terminals exploiting the XOR operation where the combined data is re modulated and retransmitted. The two terminals will find out the desired data by XOR ing the received data with its own transmitted data [237, 238]. 5.4 FULL DUPLEX TRANSMISSION In full duplex mode shown in Fig.5.6, R amplifies and forwards the signals to the destination while receiving the signals from S, which saves the time and space resources of transmission and can obviously bring in an improvement of the capacity. As transmission and reception is simultaneous on the same frequency in full duplex mode, practical implementations suffer a self interference and signal leakage between transmission and reception at R. Fig.5.6 Full duplex module 5.5 CLASSIFICATION OF RELAY SCHEMES Author‟s in [239] has discussed about the wide variety of relay schemes. According to the processing functionality of relay node, the cooperative communication scheme can be classified into Amplify and Forward (AF) [245,246] , Decode and Forward (DF) and compress and forward (CF)[240]. 5.5.1 Amplify and Forward (AF) Relay In AF scheme, R simply captures the signal from S, amplifies it, then retransmits a noisy version of source transmission. Therefore, AF relay can be considered transparent to modulation and coding techniques which are performed at S and D. Since AF relay do not need decoding, it has low complexity transceivers T R D
- 104. 81 and requires less power for processing. One of the key parameter in AF relaying design is the amplifying gain. There are two types of amplifying gain, i.e., variable gain which depends on the instantaneous channel fading of the received path to choose the amplification gain and fixed gain which is constant and depends on the fading channel statistics. Variable gain relaying generally outperforms the fixed gain relaying system. However, the requirement of instantaneous channel information will rise the complexity and cost of R. While in fixed gain relaying, R uses long term statistics of inward channel for designing the amplification gain. Therefore, fixed gain relaying does not require the instantaneous CSI and is also known as semi blind relay. The advantage of fixed gain relaying is R, does not need CSI from S and therefore system overhead and complexity is less. Due to its lower complexity, the fixed gain AF relaying scheme is preferred in the system which has a strict requirement of transmission delay. 5.5.2 Decode and Forward (DF) Relay In DF scheme, R implements a full physical layer transceiver. R decodes the signals received from S, reencodes the entire received signal and retransmits to the destination. In order to achieve the maximal diversity order, R should be able to know whether or not it has decoded correctly and then adaptively transmit the re encoded signal based on the obtained knowledge. Generally error correction codes are used for DF relaying to detect and correct the errors at R node. Under reasonable channel conditions, the regenerated signal can be same as S signal when all transmission errors between S and R are corrected by channel coding. Fig. 5.7 compares the signal processing in AF and DF relays. As illustrated in this figure, DF relay generates the error free retransmitted signal which is equal to the signal transmitted by S. Such ability can be realized through some sophisticated mechanisms, such as error detecting codes and appropriate SNR thresholds at the relay. However, these mechanisms will increase the complexity of the system. On the other hand, if R forwards the decoded signals blindly, the system performance will be degraded by errors at R which are propagated to the
- 105. 82 destination. Compared to DF relay, the noise and fading of the two hop channel is accumulated with the desired signals along with the transmission path in AF relay system. Although, AF relay does not suffer from the error propagation problem as DF relay, no hard decision is performed on the received signal at R. Fig. 5.7 Comparison of AF and DF relay 5.5.3 Compress and Forward (CF) Relay Relays with CF module are also called Estimate and Forward (EF) [241, 242], Observe and Forward (OF) [128], or Quantize and Forward (QF) [243]. CF is similar to DF module. But, unlike DF, CF goes beyond the quantization process by applying S coding techniques on the received signal [244]. Thus, CF module can be regarded as a hybrid solution of DF and AF modules. The received signal is not decoded at R. But, the signal is quantized and source coded before it is transmitted. So, the transmitted signal contains estimation errors. At the destination, the relay estimation can be used as additional information while coding the signal approaching through direct link. 5.6 MIMO OFDM SYSTEM WITH RELAY The block diagram of MIMO OFDM transmitter and receiver using relay is shown in Fig.5.8. The incoming data are first converted, coded and converted from a serial bit streams into blocks of N complex signal samples. On each of these blocks, an IFFT is performed. After parallel to serial conversion the data are transmitted to S AF D DF
- 106. 83 receiver through R (different types of R model described in section 5.2) using MIMO system. In the receiver to detect the original data reverse process of transmitter takes place. Fig.5.8 Block Diagram of MIMO OFDM transmitter and receiver with relay 5.7 MIMO MC CDMA SYSTEM WITH MPCE BASED MMSE EQUALIZATION, ITBF AND NR The proposed model of MIMO MC CDMA system incorporating channel equalization, MPCE, ITBF and novel relay (NR) is shown in Fig.5.9. At the end of OFDM modulation the signals are beam formed and transmitted to both the Relay and destination through multiple antennas. The novelty of the proposed algorithm lies in the selection of relay link between S and D and selection of number of antennas based on link quality and service required. The destination node combines the S node signal in the first phase and relayed signal in the second phase. R Mapping Serial to parallel Pilot insert IFFT Add Cyclic Prefix Parallel to serial Demap Serial to Parallel Remove Cyclic Prefix FFT Pilot Extract Channel Estimation Parallel to Serial Data In Data Out
- 107. 84 Fig.5.9 Block diagram of MIMO MC CDMA system with MPCE based MMSE, ITBF and NR Transmitter CP Insertion IFFT P/S ITBF Unit Pilot insertion based on MPCE MappingData PN Sequence S/P Detector CP removal FFT S/P PN Sequence P/S MMSE Equalization MPCE Estimated Data First time slot Second time slot Receiver Relay Relay Beam forming with Relay selection 84
- 108. 85 S 𝐵(𝑛) 𝑦𝑑 (𝑛) (𝑡2) 𝑦𝑑 (𝑛) (𝑡1) 𝑦𝑟 𝑛 (t1) 𝑥 𝑟 𝑛 (t2) 𝐻𝑑 (𝑛) 𝐻𝑟 (𝑛) 𝐻𝑠 (𝑛) Fig. 5.10 shows three nodes AF relay MIMO MC CDMA system where the S node transmits information to the destination node through R node. The S, R, and D node are equipped with Ns, Nr and Nd antennas, respectively. The communication process between S and D node is completed in two time slot, to avoid the interference between direct paths and R links. In the first time slot, the signal sequence is modulated by NC subcarriers, ( ) , 1,.....,n b cN n N as number of symbols in the nth subcarrier and it is transmitted to R as well as D. In second time slot the R transmit the amplified version of signal to D. Fig. 5.10 MIMO MC CDMA with three node AF relay The received signal at R and D node can be expressed as [251] ( ) ( ) ( ) ( ) ( ) ( ) ( ), 1,......n n n n r s r c y t H x t t n N (5.1) ( ) ( ) ( ) ( ) ( ) ( ) ( ), 1,......n n n n d d d c y t H x t t n N (5.2) where ( ) ( ) ( ) ( ) ( ), 1,...... ,n n n cx t P s t n N is the channel pre coding matrix for the S signals at the nth subcarriers. The ( ) ( )n ry t and ( ) ( )n r t are the received signal and the noise vectors at the relay node, respectively, ( ) ( )n dy t and ( ) ( )n d t are the received signal and the noise vectors at the destination node at time t, respectively. In the second slot (t+1), the relay node amplifies the received signal vector at the nth subcarrier with an Nr × Nr matrix F(n) and transmits the amplified signal vector to the destination node.
- 109. 86 ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )( ) ( ) ( ) ( 1)( 1) n n n n n n n n r s r r d n d H F H x t H F t ty t (5.3) The total received signal at the destination node over two time slot is expressed using expression (5.1) and (5.3) as ( ) ( ) ( ) ( 1) ( ) ( ) n dn n d y t y t y t (5.4) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( 1) ( ) ( ) n n n n n n n n r s r r d n n n d d F FH H x t H t t H x t t (5.5) where ( ) ( 1)n dy t is received signal at the destination node at time t+1, ( )n rH is an Nd × Nr MIMO channel between the relay and destination nodes, ( )n sH is an Nr× Ns MIMO channel matrix between the source and relay nodes, ( )n dH is an Nd × Ns MIMO channel matrix between the source and destination nodes, F(n) is the amplified signal vector at the relay node and ( ) ( 1)n d t is the noise vectors at the destination node at t+1. If BF is incorporated in the transmitter the equation (5.1 and 5.2) can be expressed as, ( ) ( ) ( ) ( ) ( ) 1 1 1( ) ( ) ( ), 1,......n n n n n r s r c t H x t t n Ny B (5.6) ( ) ( ) ( ) ( ) ( ) 1 1 1( ) ( ) ( ), 1,......n n n n n d d d c y t H x t t n NB (5.7) where B(n) is the beam form vector, ( ) 1( )n r t and ( ) 1( )n d t are the noise vector at the relay and destination node respectively. In the second slot (t2), the relay node amplifies the received signal vector at the nth subcarrier with an Nr × Nr matrix F(n) and transmits the amplified signal vector to the destination node. It is expressed as ( ) ( ) ( ) 2 1( ) ( ), 1,......n n n r r cx t F y t n N (5.8)
- 110. 87 The received signal vector at the nth subcarrier of the destination node via the S-R-D can be expressed using the expression (5.6) and (5.8) as ( ) ( ) ( ) ( ) 2 2 2( ) ( ) ( )n n n n d r r dy t H x t t ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 1 1 2( ) ( ) ( )n n n n n n n n r s r r d H H x t H t tF F (5.9) The total received signal at the destination node over two time slot is expressed using expression (5.7) and (5.9) as ( ) ( ) ( ) 2 1 ( ) ( ) ( ) n dn n d y t y t y t ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 1 1 2 1 1 ( ) ( ) ( ) ( ) ( ) n n n n n n n n r s r r d n n n n d d H F H x t H F t t H B x t t (5.10) where ( ) 2( )n dy t and ( ) 2( )n d t are the received signal and the noise vectors at the destination node at time t2, respectively. Finally the signals are equalized with help of channel estimation described in chapter 3. 5.8 SIMULATION RESULTS AND DISCUSSION MIMO MC CDMA system with MPCE based MMSE, ITBF and NR is simulated using MATLAB with the parameters given in Table 5.1 and BER is evaluated by varying Eb/No. After incorporating NR, the system performance with equalization, ITBF and NR is compared with the system with equalization and ITBF.
- 111. 88 Table 5.1 Simulation parameter of the system with MPCE based MMSE, ITBF and NR Spreading Codes Walsh Hadamard Code Number of SC 64 /128 Channel Rayleigh fading Modulation QPSK/16QAM Antennas 2x2/4x4 Equalization/Estimation MMSE/MPCE 5.8.1 Performance of the System with MPCE Based MMSE, ITBF and NR (QPSK) Fig. 5.11 shows the BER performance of MIMO MC CDMA for 128 SCs with 4x1x4 antenna configuration using QPSK modulation. The BER of the NR with ITBF is reduced as the BF improves gain in the desired direction and rejects the interference. Further by increasing the space diversity the simulation is repeated and the results are depicted in Fig. 5.12 and can be inferred that the performance is further improved due to the spatial diversity and the improvement in the reliability of the link. Fig. 5.11 Performance of 4 x 1 x 4 antenna configuration for 128 SCs (QPSK) 0 5 10 15 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMAwith MPCE based MMSE MIMO MC CDMAwith MPCE based MMSE & ITBF MIMO MC CDMAwith MPCE based MMSE, ITBF & NR
- 112. 89 Fig 5.12 Performance of 4 x 2 x 4 antenna configuration for 128 SCs (QPSK) Table 5.2 Impact of MPCE, ITBF and NR (QPSK) at the target BER of 10-3 MIMO MC CDMA system Eb/No (dB) 12 System with MMSE Eb/No (dB) 9.8 System with MPCE based MMSE Eb/No (dB) 10 System with MPCE based MMSE and ITBF Eb/No (dB) 7.2 System with MPCE based MMSE, ITBF and NR Eb/No(dB) 4.2(Single antenna) 2.8(Two antenna) Table 5.2 shows numerical result of MIMO MC CDMA system with MPCE based MMSE, ITBF and NR for the target BER of 10-3 . Initially the Eb/No of the MIMO MC CDMA system is 12 dB which is reduced to 9.8 dB using equalization with ICE (discussed in chapter 2). Practically ICE is not possible therefore the proposed system is tested with the actual channel estimation using pilot signal. Hence the MPCE based MMSE increases the Eb/No requirement from 9.8 dB to 0 5 10 15 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No BER MIMO MC CDMA MIMO MC CDMA with MPCE based MMSE MIMO MC CDMA with MPCE based MMSE & ITBF MIMO MC CDMA with MPCE based MMSE, ITBF & NR
- 113. 90 10 dB. Equalization and channel estimations are carried out in the receiver when the channel is affected by multipath fading effect. To improve the channel quality ITBF is incorporated in the transmitter side that allows the transmitter to concentrate the transmitted power onto a desired spatial direction; thus augmenting the received SNR at the intended receiver and drastically reducing the interference of non desired ones. Thus the ITBF technique reduces the Eb/No requirement from 10 dB to 7.2 dB. The channel quality between the transmitter and receiver is improved by NR which in turn reduces the Eb/No to 4.2 dB for NR with single antenna and 2.8 dB for NR with two antennas. After incorporating all the techniques the Eb/No requirement is reduced from 12 dB to 2.8 dB thus increasing the overall performance due to the reduction of ISI, increase in SNR and improvement in link reliability. 5.8.2 Performance of the System with MPCE Based MMSE, ITBF and NR (16QAM) The performance of the system is also tested with 16 QAM modulation and the simulation result is shown in the Fig.5.13 and Fig. 5.14 which depict the BER performance of MIMO MC CDMA for 64 SCs with 2x1x2 and 2 x 2 x 2 antenna configuration respectively. The BER of the ITBF with NR is reduced as ITBF improves gain in the desired direction, rejects interference and relay improves the link reliability. Further by increasing the SCs (128) the simulation is repeated and the results are depicted in Fig. 5.15 and 5.16 where performance is further improved due to the frequency diversity. It can also be inferred that increase in relay increases the spatial diversity and the reliability of link which in turn improves the system performance. Fig. 5.17, Fig.5.18, Fig 5.19 and Fig 5.20 shows the performance of MIMO MC CDMA system for 4 x 4 antennas configuration with ITBF and NR techniques for 64 SCs and 128 SCs respectively.
- 114. 91 Fig.5.13 Performance of 2 x 1 x 2 antenna configuration for 64 SCs (16 QAM) Fig.5.14 Performance of 2 x 2 x 2 antenna configuration for 64 SCs (16 QAM) 0 5 10 15 20 25 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA with MPCE based MMSE MIMO MC CDMA with MPCE based MMSE & ITBF MIMO MC CDMA with MPCE based MMSE,ITBF & NR 0 5 10 15 20 25 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA with MPCE based MMSE MIMO MC CDMA with MPCE based MMSE & ITBF MIMO MC CDMA with MPCE based MMSE, ITBF & NR
- 115. 92 Fig. 5.15 Performance of 2 x 1 x 2 antenna configuration for 128 SCs (16 QAM) Fig. 5.16 Performance of 2 x 2 x 2 antenna configuration for 128 SCs (16 QAM) 0 2 4 6 8 10 12 14 16 18 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA w ith MPCEbased MMSE MIMO MC CDMA w ith MPCEbased MMSE& ITBF MIMO MC CDMA w ith MPCEbased MMSE, ITBF & NR 0 2 4 6 8 10 12 14 16 18 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA w ith MPCEbased MMSE MIMO MC CDMA w ith MPCEbased MMSE& ITBF MIMO MC CDMA w ith MPCEbased MMSE, ITBF & NR
- 116. 93 Fig. 5.17 Performance of 4 x 1 x 4 antenna configuration for 64 SCs (16 QAM) Fig. 5.18 Performance of 4 x 2 x 4 antenna configuration for 64 SCs (16 QAM) 0 2 4 6 8 10 12 14 16 18 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA with MPCE based MMSE MIMO MC CDMA with MPCE based MMSE & ITBF MIMO MC CDMA with MPCE based MMSE, ITBF & NR 0 5 10 15 20 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA with MPCE based MMSE MIMO MC CDMA with MPCE based MMSE & ITBF MIMO MC CDMA with MPCE based MMSE, ITBF & NR
- 117. 94 Fig. 5.19 Performance of 4 x 1 x 4 antenna configuration for 128 SCs (16 QAM) Fig. 5.20 Performance of 4 x 2 x 4 antenna configuration for 128 SCs (16 QAM) 0 2 4 6 8 10 12 14 16 18 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA w ith MPCEbased MMSE MIMO MC CDMA w ith MPCEbased MMSE& ITBF MIMO MC CDMA w ith MPCEbased MMSE, ITBF & NR 0 2 4 6 8 10 12 14 16 18 10 -4 10 -3 10 -2 10 -1 10 0 Eb /No (dB) BER MIMO MC CDMA MIMO MC CDMA w ith MPCEbased MMSE MIMO MC CDMA w ith MPCEbased MMSE& ITBF MIMO MC CDMA w ith MPCEbased MMSE, ITBF & NR
- 118. 95 Table 5.3 Impact of MPCE based MMSE, ITBF and NR (16 QAM) at the target BER of 10-3 Antenna Confi. MIMO MC CDMA Eb/No (dB) MIMO MC CDMA with MMSE Eb/No (dB) MIMO MC CDMA with MPCE based MMSE Eb/No (dB) MIMO MC CDMA with MPCE based MMSE and ITBF Eb/No (dB) MIMO MC CDMA with MPCE based MMSE, ITBF and NR Eb/No (dB) 1 antenna 2 antenna 2 x 2 /64 18.5 15.2 17.6 14.2 13 11.1 2 x 2/128 16 13.4 15 11.8 10.6 8.6 4 x 4 /64 15.2 12 13.5 10.3 8 7.2 4 x 4/128 14 10.8 12 7.8 6 3.8 The performance of MIMO MC CDMA with MPCE based MMSE equalization, ITBF and NR are tested for 16 QAM and the numerical results are shown in Table 5.3.The Eb/No requirement for a target BER of 10-3 is given for various antenna configurations, SCs, ITBF and NR. From the table 5.3 it is noted that the Eb/No requirement for a target BER of 10-3 is reduced from 18.5 dB to 3.8 dB due to MPCE based MMSE,ITBF, NR and increase in number of antennas and SCs. 5.9 SUMMARY This chapter discusses the improvement in quality of wireless link of MIMO MC CDMA due to the incorporation of NR techniques. Improvement in performance due to relaying is analyzed through simulation and can be inferred that relay with single antenna improves the performance by 8.45% for 2 x 2/64 SCs, 10.16% for 2x2/128 SCs, 22.33% for 4x4/64 SCs and 23.07% for 4x4/128 SCs case. It can also be noted, multiple antenna relays increases the performance further by around 10% to 36.66% for different number of antennas in transmitter receiver with different SCs for 16 QAM modulation.
- 119. 96 CHAPTER 6 CONCLUSION This chapter concludes the present work with a summary of research contributions and highlights the merits of various techniques proposed for BER reduction. It throws open problems for further research in this area. The communication over wireless channel has three fundamental distinctions from the wireline communication. The first is the large-scale and small scale fading, second is the interference between the transmitter and receiver and the third is the user mobility in the network. The presence of fading, interference and mobility makes the design of wireless communication system challenging. The conventional design focusing on the reliability of the connection needs to mitigate the fading and multipath effects. Modern wireless system design focusing on the spectral efficiency gains from the rich multipath environment by means of utilizing bandwidth and spatial diversity through multicarrier modulation and MIMO communication. However, possible potential gain in spectral efficiency is challenged by the receiver‟s ability to accurately detect the symbol. If ISI arises within a system, the receiver output becomes erroneous at the decision device. This is an unfavorable result that should be reduced to the most minimal amount possible. Error rates from ISI are minimized through the use of equalization techniques. The performance of the equalizer depends on the quality of the channel estimate. The channel frequency response is typically not known at the receiver in advance and changes over time for a time varying channel. Therefore, it is necessary to perform channel estimation at the receiver prior to equalization. It is preferable to perform channel estimation in the frequency domain to directly obtain the channel frequency response. This can be accomplished by transmitting a known
- 120. 97 training signal. The CSI obtained from channel estimation is not only used for equalization, but it is also possible to improve the channel quality between the transmitter and the receiver using BF in the transmitter. A beamforming method is proposed which helps to improve the receiver performance without degrading any benefit of a MIMO MC CDMA. To further increase the throughput and extend the coverage of cellular networks the relay networks can be incorporated in the MIMO MC CDMA system. 6.1 RESEARCH CONTRIBUTIONS The MMSE equalization technique is proposed for MIMO MC CDMA system and the performance of the receiver is obtained through simulation and it is compared with MC CDMA and MIMO MC CDMA system. Simulation has been carried out for different antenna configuration (2x2, 3x3 and 4x4) and different SCs (16, 64 and 128) for BPSK, QPSK and 16 QAM modulations. It is inferred from the result that the performance of the MIMO MC CDMA without equalization for QPSK (128SCs/4 x 4) modulation shows 22.6% improved performance compared with the existing MC CDMA system due to spatial diversity. Further the performance of proposed MIMO MC CDMA with MMSE equalization is compared with MIMO CDMA system where the former shows18.33% improved performance due to reduction of ISI. It can also be inferred that increase in number of transmitting and receiving antennas and increase in number of SCs improves the performance due to diversity and multiplexing gain. Performance of the proposed system can still be optimized by MIMO MC CDMA with MPCE since MIMO MC CDMA with Equalization under ICE is practically not possible. To make the system more realistic, MPCE is incorporated and the system performance is tested for different antenna configuration, different SCs and modulation. As the estimation here closely matches with the realistic situation, the performance of the MIMO MC CDMA with equalization by MPCE is 16.66 % which is 1.67% inferior than the system with equalization under ICE for
- 121. 98 QPSK (128/4 x 4). However the performance is improved by increasing the antenna diversity and frequency diversity for different modulation. MPCE based MMSE equalization is incorporated in the receiver to improve the system performance when the channel is affected by multipath fading. To reduce the effect of multipath fading in the channel and to improve the link performance, the transmitter is equipped with ITBF in which an array of antennas is directed to the desired user, to improve the reception quality. By employing ITBF in MIMO MC CDMA system with MPCE based MMSE equalization, the performance increases in terms of BER. The simulation is repeated for different antenna configuration and SCs and the result infers the performance of proposed system is improved by 28% than the MIMO MC CDMA with equalization. Further to improve the system performance co operative communication through AF relay node is incorporated in the proposed system. Simulation is carried out for MIMO MC CDMA with MPCE based MMSE equalization and ITBF using NR with single antenna (2x1x2,4x1x4) and NR with two antennas (2x2x2,4x2x4). From the result it is inferred that the performance of the proposed NR with single antenna improves 41.66 % than the MIMO MC CDMA with MPCE based MMSE equalization and ITBF. Hence the Losses and noise are due to spatial diversity and reliability of link provided by NR. Eventhough the proposed system has more hardware complexity due to MPCE based MMSE equalization, ITBF and NR, it decreases the Eb/No requirement considerably reducing the transmitter power of the network by 41.66 % . This helps to ensure safe power density level of 0.1 µW/m2 to 100 µW/m2 stipulated by different countries and avoids harm to the living beings, thus ensuring eco friendly communication through reduction of electromagnetic intensity in the air.
- 122. 99 6.2 SCOPE FOR FUTURE WORK The following are some of the ideas and potential problems that might be interesting for researchers to pursue in future. - Efforts may be made to reduce the interference through joint transmit and receive BF . - It will be interesting that the performance of the proposed system can be improved by employing BF technique in the relay nodes also. - In the proposed system DF and CF can be deployed but it increases the complexity. So if the complexity is reduced, DF and CF can be used instead of AF relay in the proposed system to avoid noise propagation in the channel. - The proposed system consists of single relay system with multiple antennas. To further increase the throughput and extend the coverage in cellular network it is possible to add multiple relays with multiple antennas.
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- 149. 126 LIST OF PUBLICATIONS 1. N. Tamilarasan and L. Nithyanandan, “Performance of the MIMO MC CDMA System with MMSE Equalization”, International Journal on Recent Trends in Engineering and Technology ACEEE, vol. 6, no. 2, pp. 223-225, Nov. 2011. (ISSN 2158-5563). 2. N. Tamilarasan and L. Nithyanandan, “Performance of MIMO MC CDMA system with Channel estimation and MMSE Equalization”, Journal of Theoretical and Applied Information Technology, vol. 59 no.2, pp. 436-441, Jan. 2014. (ISSN 1992-8645). 3. N. Tamilarasan and L. Nithyanandan, “Performance of the MIMO MC CDMA System with Beamforming”, Proceedings of the 4th IEEE International Conference on Computing, Communication and Networking Technologies, pp.1-5, Jul.2013. (DOI: 10.1109/ICCCNT.2013.6726725). 4. N. Tamilarasan and L. Nithyanandan, “Performance of MIMO MC CDMA System with Channel Estimation, Equalization and Beamforming”, Proceedings of the International Conference on Advances in Communication, Network, and Computing, Elsevier, pp.661-667, Feb.2014. (ISBN 978-81-910691-7-8). 5. N. Tamilarasan and L. Nithyanandan, “Performance of MIMO MC CDMA System with Channel Estimation, Equalization and Relay”, Proceedings of the 1st IEEE International Conference on Electronics, communication and computational Engineering, pp.1166-1169, Nov.2014. (DOI:10.1109/ICECCE.2014.7086653). Journal Communicated 1. N. Tamilarasan and L. Nithyanandan, “Performance improvement of MIMO MC CDMA system using beamforming and relay”.
- 150. 127 VITAE N. Tamilarasan was born in Puducherry, India in 1980. He received his B.Tech degree in Electronics and Communication Engineering from the Pondicherry University, Puducherry, India in 2004 and consequently his M.Tech degree from Pondicherry University, Puducherry, India in 2006. Presently he is working as Assistant Professor in the Department of Electronics and Communication Engineering, Shri Krishnaa College of Engineering and Technology, Puducherry, India. He has published 9 papers in national and international conference proceedings and journals, inclusive of the ones listed. His area of interest includes Wireless Communication and Computer Networking.