Performance Enhancement in SU and MU MIMO-OFDM Technique for Wireless Communication: A Review

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 5, October 2017, pp. 2459~2467
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i5.pp2459-2467  2459
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Performance Enhancement in SU and MU MIMO-OFDM
Technique for Wireless Communication: A Review
S. N. Raut1
, R. M. Jalnekar2
Vishwakarma Institute of Technology, Pune-37, Maharashtra, India
Article Info ABSTRACT
Article history:
Received Jan 2, 2017
Revised Jun 5, 2017
Accepted Aug 11, 2017
The consistent demand for higher data rates and need
to send giant volumes of data while not compromising the quality of
communication has led the development of a new generations of wireless
systems. But range and data rate limitations are there in wireless devices. In
an attempt to beat these limitations, Multi Input Multi Output (MIMO)
systems will be used which also increase diversity and improve the bit error
rate (BER) performance of wireless systems. They additionally increase the
channel capacity, increase the transmitted data rate through spatial
multiplexing, and/or reduce interference from other users. MIMO systems
therefore create a promising communication system because of their high
transmission rates without additional bandwidth or transmit power and
robustness against multipath fading. This paper provides the overview of
Multiuser MIMO system. A detailed review on how to increase performance
of system and reduce the bit error rate (BER) in different fading environment
e.g. Rayleigh fading, Rician fading, Nakagami fading, composite fading.
Keywords:
BER
MIMO-OFDM
MMSE
Nakagamifading
Rayleigh fading
Rician fading
STBC
ZF Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
S.N.Raut
Vishwakarma Institute of Technology, Pune-37,
Maharashtra, India.
Email address:shital.raut@vit.edu
1. INTRODUCTION
Maxwell has been predicted that existence of EM waves in 1867 and Marconi transmitted radio signals
across Atlantic Ocean in the 1901. Since then there has been rapid advancements in the field of wireless
communication. Wireless networks widely used today include: cellular networks, wireless mesh networks
(WMNs), wireless Local Area Networks (WLANs), personal area networks (PANs), and wireless sensor
networks (WSNs).
The first generation mobile communication systems were analog. The 2nd generation (2G) used digital
multiple access technology. The 2G system (called GSM), furthermore its add-ons such as HSCSD and
GPRS provided data rates of 22.8 Kbps to 172.2 Kbps[1,2]. EDGE systems supported on GSM, with two
add-ons particularly increased circuit switched data (ECSD) and the increased general packet radio service
(EGPRS) has become very hip. The maximum data rate of the EDGE system is 473.6 Kbps. The third
generation (3G) provided data transfer rates greater than 2 Mbps. Universal Mobile Telephone System
(UMTS) could be a leading technology for 3G systems. WCDMA is the air-interface technology for UMTS.
The next generation is Internet Protocol (IP) based mostly and known as fourth generation (4G). LTE -
advanced which is one of the system standards for 4G is expected to achieve data downloadrates of about 3
gigabits per second and upload rates as high as 1.5 Gbps [1,2] .With evolution in mobile communication
standards, the demand for higher data rates has increased. In a single carrier transmission (SISO) to achieve
higher data rates, larger bandwidth is required. But the available spectrums are limited and hence stringent
constraints are imposed on its use. Thus it is necessary to pack more number of bits per Hz of bandwidth [3].
A MIMO (multiple-input multiple-output) system has multiple antennas at both transmitter and receiver sides

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as shown in Figure 1. Each antenna element utilizes the similar time and frequency resources facultative
capacity to be enhanced without increase in bandwidth or increased transmits power. MIMO systems provide
a promising solution for further wireless communications systems. In the MIMO systems the sender and the
receiver antennas communicate in such a way that the quality (the bit error rate (BER) or the data rate) for
each user is improved [3-5]. The wireless communication systems also suffer from multipath fading which
gives rise to higher noise and thus the high bit error rate. MIMO systems resort to spatial diversity to combat
fading [4]. For this purpose Space Time Block Coding (STBC) is employed which exploits spatial domain
along with time domain. STBC creates redundancy and thus reduces outage probability of the transmitted
signal. It is thus possible to achieve high reliability, high spectral efficiency and high performance gain. The
goal of STBC is to find code matrices that satisfy certain optimality criteria.
Figure 1. MIMO system representation Figure 2. OFDM system representation
The MIMO idea becomes even a lot of attractive in multiuser scenarios wherever the network
capability will be accumulated by simultaneously accommodating many users without the expense of
valuable frequency resources. Multiuser access of MIMO system is that the actual demand in mobile
communication. On the other hand, because of high spectral efficiency and resistance to multipath fading, the
OFDMA (Orthogonal Frequency Division Multiple Access) could be a promising candidate for high speed
wireless multiuser communication networks, such as 3GPP long term evolution advanced (LTE-A), IEEE
802.16 worldwide ability for microwave access (Wi-MAX), and IEEE 802.22 wireless regional area
networks (WRAN). In Associate in Nursing OFDMA system as shown in Figure 2, the fading coefficients of
various subcarriers are likely to be statistically freelance for various users. With channel state information at
the transmitter (CSIT), the maximum system capacity can be achieved by selecting the best user for every
subcarrier and adapting the respected transmits power. Recently, an increasing interest in multi-media
services such as video conferencing and online high definition (HD) video streaming has tends to an
incredible demand for high data rate communications with certain guaranteed quality of service (QOS)
properties. The mix of MIMO and OFDMA is taken into account a viable solution for achieving these high
data rates. In fact, the data rate improvement due to multiple antennas is unlimited if we allow the numbers of
antennas employed at both the transmitter and also the receiver to grow. Yet, the benefits of MIMO and
OFDMA do not come for free. They have significant financial implications for service providers due to the
apace increasing price for energy consumption in circuitries that is usually overlooked in the literature.
2. RESEARCH METHOD
2.1 For single user MIMO system:
Antennas 𝑇𝑥1
, . . . .. , 𝑇𝑥 𝑁 𝑇
respectively send signals 𝑥1 , . . .. , 𝑥 𝑁 𝑇
to receive antennas 𝑅 𝑥1
, . . .
𝑅 𝑥 𝑁 𝑅
. Each receives antenna combines the incoming signals which coherently add up. The received signals
at antennas, 𝑅 𝑥1
, . . . 𝑅 𝑥 𝑁 𝑅
are respectively denoted by 𝑦1 , . . .. , 𝑦 𝑁 𝑅
. We express the received signal at
antenna 𝑇𝑥 𝑞
; q =1, . . . , 𝑁𝑅 as:
𝑦𝑞 = ∑ ℎ 𝑞𝑝
𝑁 𝑇
𝑝=1
. 𝑥 𝑝 + 𝑏 𝑞 ; q = 1 , . . . , 𝑁𝑅 (1)
The flat fading MIMO channel model is described by the input-output relationship as:

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Performance Enhancement in SU and MU MIMO-OFDM Technique for Wireless… (S.N. Raut)
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y = H .x + b
2.2 For Multiuser MIMO systems:
The received signal vector at user 𝑈 𝑘; k = 1. . .k is expressed as :
𝑌𝑘 = 𝐻 𝑘 . Vt
(𝑘)
𝐵𝐹
. 𝑥 𝑘 + ∑ 𝐻 𝑘 . Vt
(𝑗)
𝐵𝐹
𝐾
𝑗=1, 𝑘≠𝑗
. 𝑥𝑗+ 𝐵 𝑘; k = 1, . . . K (2)
Where:
 𝐻 𝑘( 𝑀 𝑘 × 𝑁) is the complex channel matrix between receiver 𝑈 𝑘 and the transmit base station.
 𝐵 𝑘( 𝑀 𝑘 × 1) is an additive noise signal vector.
 𝑉𝑡 𝐵𝐹
(𝑘)
(N × 1) is the transmit beamforming vector of index k. The transmit beamforming matrix is :
𝑉𝑡 𝐵𝐹 = [ 𝑉𝑡 𝐵𝐹
(1)
, . . . . , 𝑉𝑡 𝐵𝐹
(𝑘)
]
At the receive side, beamforming vectors are denoted by:
𝑉𝑟𝐵𝐹
(𝑘)
( 𝑀 𝑘 × 1).
𝑉𝑟𝐵𝐹
(𝑘)
= [ 𝑣𝑟𝐵𝐹
(1)
, . . .. . 𝑣𝑟𝐵𝐹
(𝑀 𝑘)
] 𝑇
The resulting signal at user 𝑈 𝑘is :
𝑧 𝑘 = 𝑌𝑘
∗
. 𝑉𝑟𝐵𝐹
(𝑘)
; k = 1, . . . . K. (3)
OFDM (Orthogonal Frequency Division Multiplexing):
OFDM consists of many carriers. Thus the complex signals 𝑆𝑠 (𝑡) is represented by:
𝑆𝑠(𝑡) =
1
𝑁
∑ 𝐴 𝑛
𝑁−1
𝑛=0 (𝑡)𝑒 𝑗[𝜔 𝑛 𝑡+𝜑 𝑛(𝑡)]
(4)
Where
𝜔 𝑛= 𝜔0 + n∆𝜔
This is of course a continuous signal. If we consider the waveforms of each component of the signal
over one symbol period, then the variables 𝐴 𝑐(t) and 𝑓𝑐(t) take on fixed values, which depend on the
frequency of that particular carrier, and so can be rewritten:
𝜑 𝑛(𝑡) 𝜑 𝑛
𝐴 𝑛(t)  𝐴 𝑛
If the signal is sampled using a sampling frequency of 1/T, then the resulting signal is represented by:
𝑠𝑠(kT) =
1
𝑁
∑ 𝐴 𝑛 𝑒 𝑗[[𝜔0+𝑛 ∆𝜔) 𝑘𝑇+ 𝜑 𝑛]𝑁−1
𝑛=0
(5)
At this point, we have restricted the time over which we analyse the signal to N samples. It is
convenient to sample over the period of one data symbol. Thus we have a relationship:
t =NT
If we now simplify Eq. (5), without a loss of generality by letting w 0=0, then the signal becomes:
𝑠𝑠(kT) =
1
𝑁
∑ 𝐴 𝑛 𝑒 𝑗𝜑𝑛𝑁−1
𝑛=0
𝑒 𝑗(𝑛∆𝜔)𝑘𝑇
(6)
Now Eq.(6) can be compared with the general form of the inverse Fourier transform:

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g (kT) =
1
𝑁
∑ 𝐺 (
𝑛
𝑁𝑇
)
𝑁−1
𝑛=0
𝑒 𝑗2𝜔𝑘/𝑁
(7)
In Eq.(6), the function 𝐴 𝑛 𝑒 𝑗 𝜑 𝑛 is no more than a definition of the signal in the sampled frequency
domain, and s(kT) is the time domain representation. Eqns.(6) and (7) are equivalent if:
∆𝑓 =
∆𝜔
2𝜋
=
1
𝑁𝑇
=
1
𝑟
(8)
This is the same condition that was required for orthogonality (see Importance of orthogonality).
Thus, one consequence of maintaining orthogonality is that the OFDM signal can be defined by using Fourier
transform procedures.
3. LITERATURE SURVEY
The author Narendra M.R. in [1] present the study of transmission characteristics of MIMO system
for different modulation techniques, and evaluated the Performance evaluation of Alamouti, ZF and MLD
decoder done for 2×2 system and conclude that Alamouti gives better performance. For a MIMO system, the
STBC multiplexing techniques and ZF and MLD receivers promotes achieving better SNR performances for
digital transmission. Researcher considers only BPSK and QPSK, other systems of digital modulation gives
better performance so the problem of co-channel interference is present. Komi E. Dawui, Dirk T. M. Slock in
[2] has presented an approach based on full CSI known at transmitter and the SVD method but they have
assumed perfect channel state information at transmitter which is not always possible in practical situation. In
[3], there published that, the compare absolute SNR-based scheduling and normalized SNR-based scheduling
for heterogeneous and homogeneous wireless networks but that system did it for only Nakagami-m fading
channel because Practically there are many more fading channels.
Further Gurpreet Singh, Priyanka Mishra, Rahul Vij has done work of comparing BER for ML-
VBLAST [4] for two different modulation schemes BPSK and QPSK with different antenna configuration in
ricean and Rayleigh fading in this scheme the Maximum likelihood decoding scheme suffers from
complexity issue. Uma Singh Raghu wanshi, Prof. Pushpraj Tanwar has have been studied and analysed [5]
that ZF-VBLAST,MMSE-VBLAST,QR-VBLAST wrt their SNR and BER. But detailed information about
V-Blast is not provided. Noise or interference conditions are not clearly mentioned.
Researcher in [6], have studied the performance of cooperative OFDMA system under realistic
condition and propose transceiver structure to reduce interference between subcarriers and they assumed that
there is no channel power allocation difference between the users. D. A. Basnayaka, P. J. Smith, and P. A.
Martin in [7] try to solve macrodiversity problem using MMSE and ZF in terms of SNR with arbitarary
number of transmit and receive antennas. And the analysis did [7] is only for three base stations because they
have considered only Rayleigh fading channel. M Jung, Y Kim, J Lee, and S Choi has been [8] evaluated
that, the ergodic sum throughputs based on the optimal number of users using Monte Carlo simulations and
spatially uncorrelated MIMO channels considered. It was never mention in [8] anything about channel or
fading environment which was taken under consideration while performing this analysis. S Sun, Chih-Wei C,
Shin-Wei C, Hsiao-Hwa Chen and W Meng in [9] introduced a new type of complementary code named
3DCC which integrate STC and CDMA for MIMO system but that code did it only for two transmitting
antennas so transmit diversity is very less in this scenario. Yi Hong, E Viterbo and J C Belfiore has used
TAST block codes and perfect STBCs for such systems in order to achieve the highest possible transmission
rate and full diversity gain in [10] considered only 4 transmitting antennas, in [10] never mentioned as no. of
antennas increases what is overall effect on system. Ashu Tuli, Narender Kumar & Kanchan Sharma has
been combining 16- QAM& OFDM [11] to reduce peak to average ratio of the OFDM signal with K Fading
channel coefficient. The system is implemented for only for single user. In real they multiple users, to solve
total fading problem MIMO required with OFDM.
The MIMO scheme in [12] has been demonstratedby the researchers for the next generation
WLANs over multipath fading channel with AWGN channels. In this paper a novel hybrid space-time block
coding (HSTBC)scheme is proposed for mitigating signal fading as well as enhancing theperformance of
MIMO–OFDM based WLANs in which signal to be transmittedis first pre-coded and the resulting output is
then coded using space-timeblock code to form a HSTBC.
Wei Ni, R P Liu, J Biswas, Xin W, I. B. Collings, and Sanjay Jha has proposed new scheduling
algorithm for multiuser [13] depends on priorities. Authors prove that with this algorithm video quality goes
on increasing. In this work researcher consider only urban area the proposed mathematical limitations shows

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that there is upper bound to the number of selected users. Further in [14] derived mathematical model of
average BER and SE for MU-MIMO and shown that variable ST gives better results as compared to fixed ST
but the authors has considered only quasi-static Rayleigh fading channel and never consider inter symbol
interference and co-channel interference.
In [15], A Chahal, A Kaur R Singh introduce an orthogonal spatial division multiplexing in that
divides the central signal streams into time and frequency. Also to increase the spatial diversity we are going
to introduce spatial modulation along with STBC for our new MIMO-OSDM by considering the only
Rayleigh fading channel. Further in [16], X Wang and D Yue, Bin Lin & Yu Gong provided the study of
performance evaluation results of several codes of OSTBC with the closed-form expressions; additionally,
Monte Carlo simulation results are presented, it is demonstrate the accuracy of closed-form solutions and
checked performance of space time block code over nakagami-m channel in MIMO system.
In [17], M R. Bhatnagar, Are H and L Song have proposed that by co-ordinated transmission of
single user STBCs we can completely eliminate co-channel interference in a multiple cell scenario. A 4 × 4
both user STBC code design is presented which provides decoupled decoding of the data of each user. The
proposed multiuser STBC performs better than the existing multiuser STBCs and also provides lower value
of peak-to-average power ratio (PAPR) as compared to a trivial TDMA based STBC. In [18], the researchers
did evaluation of Alamouti, MRC decoder done with BPSK and conclude that Alamouti gives better
performance with 2×2 MIMO system Alamouti gives better results with BPSK but lot of other digital
modulation schemes are present which gives less BER. Also number of detection systems is present which
gives better result.
In “Multiuser Detection of Alamouti Signals” [19], Chee W T and A. R Calderbank presented
solutions to the multiuser detection problem for Alamouti signals over an i.i.d. Rayleigh fading multiple
access channels by A-BLAST algorithm for decoding signal and QOSTBC for modulation. To improve
performance 1 or 2 bit feedback is used to instruct the transmitter but this system provides diversity at
transmitter end not receiver end therefore BER possibility is more. Further, in [20], Researcher proposes that
if no. of transmitting antennas is 4 per user for k user with no. of receiving antennas k/2. MMSE detection
scheme gives better results than ZF. In this study the researcher never considers intersymbolinterference, co-
channel interference. Further the enhancement has done to improve the coverage of communication system
researcher derived new analytical expression in multirelay environment of MIMO system [21]. It calculates
only diversity order and coding gain problem but lot of issues related to MIMO system need to be solved. In
[22] is has been shown how MIMO-STBC using multiple transmit and receive antennas increases the
diversity, which results in high signal to noise ratio (SNR). It is observed that by using multiple transmit and
receive antennas SNR increases for single as well as for multiple users. Researcher increased number of
antenna at Tx-Rx side; diversity goes on increasing, fading decreases, SNR increases by this enhancement
MIMO solve the problem of fading but still intersymbol interference & co-channel interference is the major
issue.
Further Tanvir A, Md. Ali and Shaikh E U in [23] have compare results of different detection
methods for secured multiuser MIMO-OFDM with 2 transmitting antennas and 2 receiving antennas. With
this method, authors got Pre MMSE gives less BER for 2×2 MIMO-OFDM system but still the problem of
fading is present because diversity order is less. A Boudaa, M Bouziani has said in [24] that before SFBC
encoding, designed and implemented eigenbeamforming with pre-processing multiplexing after that the data
send on the channel with known CSI using Zero Forcing (ZF) method. Zero forcing scheme gives good
results when channel is noiseless but when channel is noisy it amplify the noise.
In [25], S K Borra has compared performance of OFDM system with 16-QAM and BPSK done and
concluded that 16-QAM gives best result over different fading environment. OFDM with 16-QAM increases
spectral efficiency but still fading is the major issue. Further Uma Raj and Vidhyacharan Bhaskar [26] has
studied the performance analysis of scheduling schemes for multiuser MIMO-OFDM systems employing
Orthogonal Space-Frequency Block Coding in η-μ fading scenario over frequency selective fading channels
and On basis of this ASE and BER are compared and presented advantages of SNR based scheduling in
MIMO-OFDM system employing OSFBC and adaptive modulation and discussed but they never checked
system for real application which is main disadvantage. Introduced a linear technique [27] that performs
successive MMSE prefiltering in order to reduce the performance loss due to the zero MUI constraint and the
cancellation of the interference between the antennas located at the same terminal. Further this paper offers
two techniques, one with combining linear pre-coding technique called successive optimization and a non-
linear technique called Tomlinson-Harashimaprecoding. Second introduced a linear technique that performs
successive MMSE preﬁltering in order to reduce performance loss. But the problem with such system is that
it is implemented only for flat fading.
Ramoni Adeogun in [28], analyzed the capacity and BER of different multiple satellite scenarios
using different models. The reseracher investigated the capacity and bit error rate (BER)performance of

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Multiple Input Multiple Output (MIMO) satellite systemswith single and multiple dual polarized satellites in
geostationary orbit and a mobile ground receiving station with multiple antennas.Further C. Manikandan [29]
investigates the effect of many antennas at the sender i.e. transmitter and receiver end of SU-MIMO and MU-
MIMO, using a SVD based single and multiple beamforming technique, for various Modulation schemes
namely ASK, BPSK, QPSK, 8-PSK and 8-QAM. But they never consider intersymbol and co-channel
interference considered only 3×3 system. This paper also states that with the increase in number of antennas
in the transmitter side and receive sides, there is a decrease in BER and thus increase in reliability. With
single beamforming, there is a further substantial decrease in the BER as compared to multiple beamforming.
There is one more investigation done by Chabalala S. Chabalala and Lebajoa Mphatsi for MIMO
performance for wireless communication systems [30] in a multiuser environment. The user information is
given by a data matrix formed by the observed input-output data of the system, from which subspace
identification method is developed using LQ-decomposition of the matrix, also the ergodic channel capacity
model has been presented and evaluated for different configurations for a MIMO system with varying
number of transmit and receive antennas but in this design also the researcher never consider intersymbol and
co-channel interference.
In [32] analyzed investigate the performance of multi user decode and forward network with a full
MIMO design in the presence of CCI and AWGN. With some scenarios, investigators applied SNR-based
selection criterion to select the Nth-best user. Furthermore, they have also presented three main remarks to
differentiate the effect of key system parameters on the diversity order and the array gain of the system which
is based on asymptotic SER expression but the investigators never consider the losses in decode and forward
network. The researcher in [31] has compared the performance of Space Time Block Coding with code rate
½ and ¾ for a Turbo Coded Multiple Input Multiple Output (MIMO) system and uncoded MIMO system.
The turbo encoder encodes the input information bits and sends to a QPSK or 16 QAM or 64 QAM
modulator. The Rayleigh fading effected received signal at receiver is a linear superposition of the
transmitted signals. It is demonstrated that there is around 7- 11 db coding gain for using code rate ½
compared to code rate ¾ of a TC coded MIMO system and there is around 8-10 db coding gain for using
code rate ½ compared to code rate ¾ of a uncoded MIMO system.
To overcome the multiuser interference problem Yuan O [33] and his team propose an analytical
model and closed form BER performance equation pf the MB- OFDM UWB systems with multiuser
interference for LOS and NLOS multipath fading channels even though they have considered the multiuser
interference problem but the ignored the interband interference and assumed static during the symbol
interval. In [34], authors investigated the reduce of the average BER for multiuser MIMO-OFDM systems
with different suboptimal detectors. For the ZF and SIC detectors, the optimal and suboptimal algorithms are
presented by applying the exhaustive search and Lagrange multiplier method and to minimize the
computational complexity. Subcarriers are allocated to users under the assumption that the transmit power is
identical over all antennas for each subcarrier. The proposed suboptimal algorithm achieves an improvement
over conventional algorithms for each detector. In [35], Jing M, C Chen, L Bai, H Xiang, and Jinho Choi
stated that optimal algorithms have a prohibitive high complexity, so introduced the low-complexity
suboptimal algorithms that separately allocate the subcarriers and power. Also considered few detectors, ML,
linear and SIC, and derived the optimal subcarrier and power allocation algorithms that are based on the
exhaustive search and numerical methods. The performance achieved by the suboptimal algorithms can be
close to that with the corresponding optimal algorithms.
The paper [36] states the investigation about MIMO-OFDM for VLC systems which assumes the
phase differences of channel matrices in the FD induced by the distance differences of the multiple
transmitter-receiver links. Different from state-of-the-art MU-MIMO VLC schemes, zeroforcing and MMSE
precoding techniques are performed on the complex channel matrices for each subcarrier, which are less
correlated when the phase differences are considered. It is also stated that the subcarrier with higher index
achieves more spectral efficiency, especially when the users are highly correlated. MMSE precoding
outperforms that with zero forcing when the optical power is low and when more performance gain is
achieved when the users are closer. In [37], MU-MIMO-OFDM is studied for indoor VLC systems. In these
investigators considers the distance differences of the multiple transmitter receiver links, which causes phase
differences in the FD. The zero forcing and MMSE precoding techniques are applied on the complex channel
matrices for each subcarrier, which are less correlated when the phase differences are considered. The paper
also introduces the two DC bias and scaling schemes for different sceneries. It has been observed that the
subcarrier with higher index achieves more spectral efficiency, especially when the users are highly
correlated. MMSE is feasible for zero forcing when the optical power is low and more performance gain is
achieved when the users are closer.
In [38], introduces a nullspace combining BD technique and SIC technique for a multiuser MIMO-
OFDM system uplink with the same subcarrier shared by multiple users named low complexity multiuser

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detection algorithm (RSZF-SIC), whose aim was to eliminate the CCI by recursively successive zero-forcing
process and successive interference cancellation process at the base station. The paper also proposed
algorithm reduced the computational complexity effectively because the total number of SVD operations and
the dimension of the SVD matrix were reduced greatly by recursively eliminating the interference based on
the nullspace orthogonal theorem. In [39], the problem of CFO estimation in the context of a single and
multi-user MIMO-OFDM systems communicating over a frequency selective wireless channel has been
addressed using the MEP criterion. This method poses the CFO estimation problem as a detection problem,
which is achieved by discretizing the continuous CFO parameter space into a finite number of discretized
bins. We have theoretically derived the statistics of the CFO estimation error and the BER degradation due to
this error using the MEP method. The performance of the proposed method is shown to be better than that of
the classic MAP and ML based methods both in the MSE and BER sense.
4. CONCLUSION
As discussed by the researchers in the various papers it is found that they have used different
technologies in wireless communication systems for analyzing performance of SU and MU MIMO system
which are used to reduce the bit error rate and to increase the quality of signal. As mentioned in the literature
of this paper each research work has its merits and demerits. Now a days requirement of data rate is high and
also improvement in signal quality by removing fading effect is required MIMO system is used in the higher
data rate where fading problems are occurred. To remove the interference the OFDM is used. Hence to
resolve such issues in wireless communication where high data rate is required we need to concatenate both
MIMO and OFDM system. This MIMO-OFDM system not only increases the data rate but also overcomes
the problem of fading in wireless communication.
ACKNOWLEDGEMENT
I would like to thank Prof.( Dr.) J.S.Chitode for valuable guidance and support. I would like to thank
the researchers as well as publishers for making their resources available. Additionally, I appreciative to
commentator for their significant recommendations furthermore I am also thankful to reviewer for their
valuable suggestions and also thank the college authorities for providing the required infrastructure and
support.
REFERENCES
[1] Narendra M R, “Study of Transmission Characteristics of MIMO System for Different Modulation
Techniques.”, International Journal of Innovative Technology and Exploring Engineering (IJITEE)
ISSN: 2278-3075, Volume-3, Issue-1, June 2013.
[2] Komi E. Dawui, Dirk T. M. Slock, “Multiuser-MIMO Downlink Tx-Rx Design Based On SVD
Channel Diagonalization and Multiuser Diversity”, Published in 2014 by Eurecom Institute 2229
route des Crêtes, B.P. 193, 06904 Sophia AntipolisCedex, France.
[3] Mohammad Torabi, David HaccounandWessamAjib, Department of Electrical Engineering, ´
EcolePolytechnique de Montréal, Montréal, QC, Canada, “Multiuser Scheduling over MIMO
Nakagami-m Fading Channels: Capacity and BER Performance”, Published in IEEE transaction (
2009).
[4] Gurpreet Singh, PriyankaMishra,RahulVij , Department of Electronics and Communication
Engineering,ShaheedBhagat Singh State Technical Campus, Ferozpur, Punjab, United Group of
Institution, Allahabad, L R of Institute of Engineering, Solan, “Performance evaluation of ML-
VBLAST MIMO decoder using differnt antenna configuration using Ricean and Rayleigh channel”,
2013 International Conference on Communication Systems and Network Technologies
[5] MicaelaLacy,Erwan Xiao, “A Brief Survey on V-Blast Mimo-Ofdm System with ZF and MMSE
techniques”, International Journal Of Scientific Progress And Research (IJSPR) ISSN: 2349 – 4689
Volume-06, Number -02, 2014.
[6] Micaela Lacy, Erwan Xiao, “Resource Allocation for Wireless Multicast MIMO-OFDM Systems
Using Water Filling Algorithm”, Published Research article in IJESC December 2014 Issue, DOI
10.4010/2014.288 ISSN-2321 -3361 © 2014 IJESC.
[7] Dushyantha A. Basnayaka, Student Member, IEEE, Peter J. Smith, Senior Member,
IEEE,andPhillipa A. Martin, Senior Member, IEEE, “Performance Analysis of Macrodiversity
MIMO systems with MMSE and ZF receivers in Flat Rayleigh fading”, IEEE Transactions On
Wireless Communications, Vol. 12, No. 5, May 2013.

 ISSN: 2088-8708
2466
[8] Minchae Jung, Younsun Kim, Juho Lee, and Sooyong Choi, “Optimal Number of Users in Zero-
Forcing Based Multiuser MIMO Systems with Large Number of Antennas”, Published in Journal
Of Communications And Networks, Vol. 15, No. 4, August 2013.
[9] Siyue Sun, Student Member, IEEE, Chih-Wei Chen, Shin-Wei Chu, Hsiao-Hwa Chen, Fellow,
IEEE, and WeixiaoMeng, Senior Member, IEEE), “Multiuser-Interference-Free Space–Time
Spreading MIMO Systems Based on Three-Dimensional Complementary Codes”, IEEE SYSTEMS
JOURNAL, VOL. 9, NO. 1, MARCH 2015.
[10] Yi Hong, EmanueleViterbo,Politecnico di Torino, Jean-Claude BelfioreENST, “A Space-Time
Block Coded Multiuser MIMO Downlink Transmission Scheme”, IEEE Transactions ISIT 2006,
Seattle.
[11] D AshuTuli, Narender Kumar, KanchanSharma,GGSIPU, New Delhi, India, “Image transmission
using M-QAM OFDM system over composite fading channel”, (IOSR-JECE) e-ISSN: 2278-2834,p-
ISSN: 2278-8735.Volume 9, Issue 5, Ver. 1 (Sep - Oct. 2014), PP 69-77.
[12] IsiakaAjewaleAlimi, Kayode Francis Akingbade, Jide Julius Popoola, Michael O. Kolawole, “An
Hybrid Coding Technique for Efficient Bandwidth Usage in conformity with IEEE 802.11 WLAN
Standard”, Published in Vol. 3, No. 5, October 2013, pp. 593~602 by International Journal of
Electrical and Computer Engineering (IJECE).
[13] Wei Ni, Member, IEEE, Ren Ping Liu, Senior Member, IEEE, JayetaBiswas, Xin Wang, Senior
Member, IEEE,Iain B. Collings, Senior Member, IEEE, and Sanjay Jha, Senior Member, IEEE,
“Multiuser MIMO scheduling for mobile video applications”, IEEE Transaction on Wireless
Communications: Paper-TW-Jul-12-1340.R2, August 2014.
[14] Xin Yin, Xiangbin Yu, Yan Liu, Wei Tan, XiaominChen,College of Electronic and Information
Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China,National Mobile
Communications Research Lab,.Southeast University, Nanjing, China, “Performance analysis of
Multiuser MIMO system with adaptive modulation and Imperfect CSI”, IEEE Transaction in 2014
[15] ArashpalChahal, AmandeepKaur, Ranjeet Singh, “An Adaptive MIMO-OSDM Prototype for Next
Generation Communication Systems under fading Channels”, International Journal of Engineering
Research and General Science Volume 3, Issue 1, January-February, 2015 ISSN 2091-2730.
[16] Xudong Wang, DianwuYue, Bin Lin, Yu Gong, “Performance Results of Space-time Block Coding
Over Nakagami-m Fading Channels”, IEEE transaction 2005.
[17] Manav R. Bhatnagar, Are Hjørungnes, Lingyang Song, “Interference cancellation by using Quasi-
Orthogonal STBC in Two-User MIMO System”, .( This work was supported by the Research
Council of Norway projects 176773/S10 called OptiMO and 183311/S10 called M2M, which
belong tothe VERDIKT program.
[18] NaziaParveen, D.S.Venkateswarlu, “Implementation of Space Time Block Coding (STBC) Using 2
Transmit and 2 Receive Antennas”, International Journal of Emerging Technology and Advanced
Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 10, October 2012.
[19] CheeWei Tan, Member, IEEE, and A. Robert Calderbank, Fellow, IEEE, “Multiuser Detection of
Alamouti Signals”, IEEE Transactions On Communications, Vol. 57, No. 7, July 2009.
[20] Rajab M. Legnain, Roshdy H.M. Hafez, and Ian D. Marsland, “Multiuser Detection for Rate-1/2
OSTBC with Four Transmit Antennas”, IEEE transaction 2013.
[21] EhsanSoleimani-Nasab, Student Member, IEEE, MichailMatthaiou, Member, IEEE, and
MehrdadArdebilipour, “Multi-Relay MIMO systems with OSTBC over Nakagami-m fading
channels”, IEEE Transactions On Vehicular Technology, Vol. 62, No. 8, October 2013.
[22] Swapnil T. Patil, Pratap N. Shinde, “Multiuser MIMO-STBC system over Rayleigh fading
channel”, Proceedings of 12th IRF International Conference, 29th June-2014, Pune, India, ISBN:
978-93-84209-31-5 .
[23] Tanvir Ahmed, Md. Mortuza Ali and ShaikhEnayetUllah, “BER Performance Analysis of a STBC
Encoded Secured Multiuser MIMO- OFDM Wireless Communication System”, International
Journal of Hybrid Information Technology Vol. 5, No. 4, October, 2012.
[24] Ahmed. Boudaa, Merahi. Bouziani, “Combining SFBC_OFDM Systems with SVD Assisted
Multiuser Transmitter and Multi user Detector” IOSR Journal of Electronics and Communication
Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735. Volume6, Issue 6 (Jul. - Aug.
2013), PP 24-28.
[25] Sai Krishna Borra, Suman Krishna Chaparala, “Performance Evaluation of OFDM system With
Rayleigh,Rician and AWGN Channels”, .(International Journal of Emerging Technology and
Advanced Engineering, (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 3,
March 2013.

IJECE ISSN: 2088-8708 
2467
[26] Uma Raj,VidhyacharanBhaskar, “Performance analysis of SNR based scheduling scheme for
MIMO OSFBC-OFDM systems in n–µ fading channels”, Int J Wireless Inf Networks (2015)
22:147–156.
[27] The partly funded by the European Union, “Multi-User MIMO downlink precoding for users with
Multiple antennas”, This work has been partially performed in the framework of the IST project
IST-2003-507581 WINNER.
[28] RamoniAdeogun “Capacity and Error Rate Analysis of MIMO Satellite Communication Systems in
Fading Scenarios”, Published in Vol. 4, No. 4, August 2014, pp. 614~622 by International Journal
of Electrical and Computer Engineering (IJECE).
[29] The partly funded by the European Union, “Performance analysis of SVD based single and multiple
beamforming for SU-MIMO and MU-MIMO systems with various modulation”, Contemporary
Engineering Sciences, Vol. 7, 2014, no. 11, 543 – 550.
[30] Chabalala S. Chabalala and LebajoaMphatsi, “Multi-User MIMO Downlink Channel Capacity for
4G Wireless Communication Systems”, IJCSNS International Journal of Computer Science and
Network Security, VOL.13 No.6, June 2013.
[31] M.M.Kamruzzaman, “Performance Comparison of Space Time Block Coding with Code Rate ½
and ¾ for Turbo Coded Multiple Input Multiple Output System”, International Journal of
Information & Network Security (IJINS) Vol.2, No.6, December 2013, pp. 482~491 ISSN: 2089-
3299.
[32] Guoxin Li∗, Jin Chen, Yuzhen Huang, GuochunRen, “Performance analysis of MIMO DF relay
network with the Nth-best user selection scheme in the presence of co-channel interference”, Int. J.
Electron. Commun. (AEÜ) 69 (2015) 745–752.
[33] “Performance analysis of the multiband Orthogonal Frequency Division Multiplexing Ultra-
Wideband systems for multipath fading and Multiuser Interference channels”, Hindawi Publishing
Corporation Mathematical Problems in Engineering Volume 2015, Article ID 190809, 9 pages.
[34] “Resource Allocation for Multiuser MIMO-OFDM Systems with Various Suboptimal Detectors”J
Mao, C Chen, and H Xiang, The 22nd Asia-Pacific Conference on Communications (APCC2016).
[35] “Subcarrier and Power Allocation for Multiuser MIMO-OFDM Systems with Various Detectors”, J
Mao, C Chen, L Bai, H Xiang, and J Choi, 978-1-5090-1698-3/16/$31.00 ©2016 IEEE.
[36] “Multiuser MIMO-OFDM for Visible Light Communications”, Qi Wang, Z Wang, and L Dai, IEEE
Photonics Journal, 2015.
[37] “Multi-User MIMO-OFDM for Indoor Visible Light Communication Systems”, Qi Wang, Z Wang,
C Qian, J Quan, and L Dai, at GlobalSIP 2015 -- Symposium on Signal Processing for Optical
Wireless Communications.
[38] “Low Complexity Multiuser Detection with Recursively Successive Zero-Forcing and SIC Based
on Nullspace for Multiuser MIMO-OFDM System”, Z Chengwen, W Bin, Li Danli, Tan X.
[39] “Minimum-Error-Probability CFO Estimation for Multi-User MIMO OFDM Systems”, Amit
Kumar Dutta, K.V.S. Hari and LajosHanzo, DOI 10.1109/TVT.2014.2349075, IEEE Transactions
on Vehicular Technology.

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