Performance Improvement Interfering High-Bit-Rate W-CDMA Third-Generation Smart Antenna systems

I. INTRODUCTION
Demand for service provision via the wireless
communication bearer has risen beyond all expectations. If
this extraordinary capacity demand is put in the context of
third-generation systems requirements (UMTS, IMT 2000)
[1], then the most demanding technological challenge
emerges: the need to increase the spectrum efficiency of
wireless networks. While great effort in second-generation
wireless communication systems has focused on the
development of modulation, coding, protocols, etc., the
antenna-related technology has received significantly less
attention up to now. In order to achieve the ambitious
requirements introduced for future wireless systems, new
“intelligent” or “self-configured” and highly efficient
systems, will be most certainly required. In the pursuit for
schemes that will solve these problems, attention has turned
into spatial filtering methods using advanced antenna
techniques: adaptive or smart antennas. Filtering in the
space domain can separate spectrally and temporally
overlapping signals from multiple mobile units, and hence
the performance of a system can be significantly improved.
In this context, the operational benefits that can be achieved
with exploitation of smart antenna techniques can be
summarized as follows [2].
1. More efficient power control;
2. Smart handover;
3. Support of value-added services:
(a) Better signal quality;
(b) Higher data rates;
(c) User location for emergency calls;
Performance Improvement Interfering High-Bit-Rate W-CDMA
Third-Generation Smart Antenna systems
Ruchi Dubey, Amitesh Raikwar and Richa Tiwari
Department of Electronics and Communication, RKDF, Bhopal, (M.P.)
(Recieved 28 April 2012, Accepted 15 May 2012)
ABSTRACT : Performance enhancement of smart antennas versus their complexity for commercial wireless
applications. The goal of the study presented in this paper is to investigate the performance improvement
attainable using relatively simple smart antenna techniques when applied to the third-generation W-CDMA air
interface. Methods to achieve this goal include fixed multi beam architectures with different beam selection
algorithms (maximum power criterion, combined beams) or adaptive solutions driven by relatively simple direction
finding algorithms. After comparing these methods against each other for several representative scenarios, some
issues related to the sensitivity of these methods are also studied, (e.g., robustness to environment, mismatches
originating from implementation limitations, etc.). Results indicate that overall, conventional beam forming
seems to be the best choice in terms of balancing the performance and complexity requirements, in particular
when the problem with interfering high-bit-rate W-CDMA 3g users is considered.
Keywords: Adaptive algorithms, smart antennas, third Generation systems, wavelength code division mupltiple access
(WCDMA).
International Journal of Electrical, Electronics &
Computer Engineering 1(1): 69-73(2011)
I
J E
E
CE
(d) Location of fraud perpetrators;
(e) Location sensitive billing;
(f) on-demand location specific services;
(g) Vehicle and fleet management;
4. Smart system planning;
5. Coverage extension;
6. Reduced transmit power;
7. Smart link budget balancing;
8. Increased capacity.
Fig .1. 900 MHz.
Much research has been performed over the last few
years on adaptive methods that can achieve the above
benefits, e.g., [3–7]. Nevertheless, it has been recognized
that communication systems will exploit different advantages
or mixtures of advantages offered by smart antennas,
depending on the maturity of the underlying system. For
ISSN No. (Online) : 2277-2626

70 Dubey, Raikwar and Tiwari
example, at the beginning, costs can be reduced by exploiting
the range extension capabilities of simple and cheap.
Fig. 2. 1800 MHz.
Smart antennas. Then costs can be further decreased
by avoiding extensive use of small cells where there is
increased capacity demand, by exploiting the capability of
smart antennas to increase capacity, with relatively simple
(more complex than the previous phase) adaptive methods.
Finally, more advanced systems (third generation) will be
able to benefit from smart antenna systems, but it is almost
certain that more sophisticated space/time filtering
approaches [6] will be necessary to achieve the goals of
universal mobile telecommunications service (UMTS),
especially as these systems become mature too. Recognizing
that full exploitation of smart antennas, and in particular in
future-generation systems, requires the growth of radio-
frequency and digital signal-processing technology, this
paper focuses on studying the performance of a UMTS-
type system [wireless code-division multiple access (W-
CDMA)], with relatively simple (in terms of complexity),
smart antenna methods [9]. The next section will describe
the simulation method that was employed in order to achieve
this goal. Then simulation results will be presented and
discussed in the context of the achieved performance under
different conditions.
II. LOW-COMPLEXITY SMART ANTENNA
Fig .3. Smart Antenna Block.
A. The W-CDMA System
(1) General Description: The universal mobile
telecommunications system (UMTS UTRA) FRAMES
mode-2 W-CDMA proposal (FMA2) is based on W-CDMA,
with all the users sharing the same carrier under the direct-
sequence CDMA (DSCDMA) principle. The frequency-
division duple Xing (FDD) mode; however, a time division
duple Xing (TDD) mode for W-CDMA is also included in
the specification. The FMA2 is asynchronous with no base
station dependence upon external timing source (e.g.,
globalpositioningsystem).
Fig. 4. 1900 MHz.
It employs 10-ms frame length, which, although it is
different from the global system for mobile communications
(GSM), also allows making intersystem handoffs, since 12
FMA2 frames are equal to a single GSM FMA defines two
types of dedicated physical channels on both uplink and
downlink: the dedicated physical control channel (PCCH)
and the dedicated physical data channel (PDCH). The PCCH
is needed to transmit pilot symbols for coherent reception,
power-control signaling bits, and rate information for rate
detection. The FMA2 downlink is similar to second-
generation DS-CDMA systems like IS-95. The PDCH and
PCCH are time multiplexed within each frame and fed to the
serial-to-parallel converter. Then, both I and Q branches are
spread by the same channelization orthogonal variable
spreading factor (OVSF) codes and subsequently scrambled
by a cell-specific code. The downlink scrambling code is a
40 960 chip segment (one frame) of a Gold code of length
21. The channelization codes are OVSF codes that preserve
orthogonality between channels with different rates and
spreading factors. Each level of the tree corresponds to a
different spreading factor.

Dubey, Raikwar and Tiwari 71
Fig. 5. 2100 MHz.
A code from the tree can be used if and only if no
other codes are used from an underlying branch or the path
to the root of the tree. All codes form the tree cannot be
used simultaneously if orthogonality is to be preserved [10].
In essence, codes generated with this method are
Walsh–Hadammard codes, with small differences in the
permuting rows of each level, in order to preserve interlevel
orthogonality. Two basic options for multiplexing physical
control channels are: time multiplexing and code multiplexing.
In FMA2, a combined IQ and code-multiplexing solution
(dual-channel quaternary phase-shift keying) is used to avoid
audible interference problems with discontinuous
transmission. This solution also provides robust rate
detection since rate information is transmitted with fixed
spreading factor on the PCCH. In terms of the uplink
spreading and scrambling concepts of the PDCH and PCCH
physical channels, the physical channels are mapped onto I
and Q branches, respectively, and then both branches are
spread by two different OVSF channelization codes and
scrambled by the complex code. Each part of the complex
scrambling code is a short Kasami code256 chips long. As
a second option, long-code complex scrambling may also
be used. Such a long code is an advantage for the
conventional receiving scheme (single-user matched filtering),
since it prevents consecutive realization of bad multiple-
access interference (MAI). However, it is a disadvantage
from the point of view of implementing multi-user detection,
since the detector must be time-varying and explicit
knowledge of interference is required.
1. Perfect power control.
2. Perfect channel estimation.
3. One chip is represented by one sample hence no pulse
shaping.
4. All users [including low bit rate (LBR)] are modeled according
to the W-CDMA UTRA frame format, and also spreading/
despreading and scrambling/descrambling are incorporated in
the simulator. This is done to take into account site-specific
radio channel models (ray tracing) where even LBR
interfering users color the spatial structure of MAI.
(5) Interfering users from other than the central cells
are modeled as space–time white noise. depicts the
simulation schematic of the desired user. Since the data from
other users are of no interest (single-user detection), the
interfering users from the same cell are further simplified.
Same-cell interferers are constructed to account for MAI
only; hence only scrambling codes are transmitted This can
be viewed also as a stream of “1” spread by the first OVSF
code depicts one way to visualize or model the transmission
of such signals through the radio channel with the help of
a bank of tapped delay lines. The values of the parameters
shown in are taken from the results produced with the help
of the ray-tracing propagation model described in the next
section. The reception process discussed above can be
described as
x(t) = ,1 , ,
1 1
, ( – ) , ( – ) ( )
K L
k k l k l
k l
pk l s t T gk l t T a n t
= =
− +∑∑
where is the received signal vector by the element
antenna array, is the number of users, is the number of
multi paths, is the power of the th multipath component
from the th user, is the scrambling code, is the antenna
response vector, is the noise vector, and is
s(t – Tk, l) = ,1 , , ,( – ) ( – )PDCH PDCH
k k l k l k lC t T b t T
, , , ,· ( – ) ( – )PCCH PCCH
k l k l k l k lj C t T b t T+
III. THE SMART ANTENNA
Conventional Beam forming Fourier Method (FM): This
classic method is based on the fact that the spatial Fourier
transform of an observed signal vector across an array
defines the spatial spectrum. The resulting antenna weights
can be expressed as
2
exp ( –1) sin( )nw j n d
π 
= ϕ 
λ 
It is a straightforward technique, and since it is fairly
insensitive to parameter variations, it is inherently robust.
In the presence of wide signal separations, this method may
offer more robust Performance than the high-resolution
methods, and since it Is far easier to compute, it is a favored
candidate in real system implementations.
Switched Beams (SB): This method uses a number of
fixed steered beams, calculates the power level at the output
of each of the beams, and in its simplest form the beam
with the highest output power is selected for reception.
Although it is believed that this algorithm is best suited to
environments in which the received signal has a well defined
direction of arrival, i.e., the angular spread of the
environment should be less than the beam width of each of
the beams, even in environments where the angular
spreading is high, there can be benefit from this algorithm.
It is not efficient when co channel interference is present,
but it may cope with frequency-selective channels provided

72 Dubey, Raikwar and Tiwari
the channel consists of narrow clusters at widely separated
directions For both of the above cases, a linear array with
eight elements was used. The weights that generate the
beams for the SB methods (as for the weights of all the
algorithms that are employed in the simulation results shown
here) are normalized to the absolute value of the weight
vector. In an attempt to balance the conflicting requirements
not to consider ideal situations (60 dB) and at the same
time not to bias the analysis at this level with high sidelobe
and null depth levels (15 dB), the minimum null depth was
chosen to be limited to 30 dB. The complexity associated
with adaptively scanning the beam-pointing direction by
varying complex weights in a beam forming network is
avoided by switching between fixed beam directions. The
weights that produce the desired grid of beams can be
calculated and saved for future use; hence the beam
switching approach allows the multi beam antenna and
switch matrix to be easily integrated with existing cell site
receivers as an applique [5]. Also, tracking is performed at
beam switching rate (compared to angular change rate for
direction finding methods and fading change rate for
optimum combining [2]). Disadvantages include low gain
between beams, limited interference suppression and false
locking with shadowing, interference, and wide angular
spread [2]. 3) Combined Switched Beam Approach (SBc):
The difference between this method and the basic switched
beam approach is that in this case, the calculated power
levels at the output of each of the beams are considered in
the context of a power window threshold (from the maximum
power), and all the beams with output power within the
employed power window are selected. The default power
windows were chosen to be 3 and 5 dB for SB13 and SB9,
respectively. These default values were chosen 1) bearing
in mind the measurements reported in [4] and also in an
attempt to balance the different beam spacing between the
two methods as well as the conflicting requirements of
capturing as much desired energy as possible and avoiding
interference. As a result, two different cases are considered:
SB13c and SB9c. Combining the best beams from a grid of
beams is slightly more complex than the basic grid of beams
approach. It requires processing the outputs from all the
beams in order to find which beams give power within the
chosen power window, and then summation of the chosen
output signals.
IV. BEAM SPACE OPTIMUM COMBINING
(BOPC)
This method works with the eigenvalues of the
calculated correlation matrix. The eigenvalues of a correlation
matrix indicate how dispersive (spatially) the received signal
is. If there are a few eigenvalues with similar amplitudes,
then the variability of the signal will tend to be confined to
the subspace spanned by the corresponding eigendirections.
If the eigenvalues are approximately equal, then the signal
spans the full multidimensional space. If a power window is
employed for the eigenvalues of the correlation matrix, then
a mechanism is automatically generated to control how many
degrees of freedom will be used. The chosen power window
can be fixed to some predefined value, or can be adaptive
to each scenario considered. After the calculation of
eigenvalues, the corresponding eigenvectors of the
covariance matrix are simply combined in an optimum manner.
From [8], for the eigenvalue solution in array space for
maximum signal-to-(interference plus noise) ratio (SINR) at
the output of a smart antenna.
wopt = –1
maxxxR v
Where is the associated eigenvector to the largest
eigenvalue of It was shown in [3] that the eigenvector that
corresponds to the maximum eigenvalue of the correlation
matrix is approximately equal to the steering vector of the
target signal source (desired signal) when the desired signal
is much stronger than the interferers at the receiver. As a
result, this technique is particularly applicable to CDMA
systems due to the available processing gain. This technique
is suboptimal in that it does not null out interference.
Although it is rather complex N N , it is very promising
since there have been ways suggested in [2] to reduce its
complexity down to (11 N). Smart antenna system combines
an antenna array with the digital signal-processing capability
to transmit and receive in an adaptive, spatially sensitive
manner. Such a system automatically changes the di
rectionality of its radiation pattern in response to the signal
environment [1]. The main objective of a smart antenna is
to implement an adaptive algorithm to achieve the optimal
weights of antenna elements dynamically. Optimality criteria,
such as minimum mean square error (MMSE), least square
error (LSE), maximum signal-to-noise-ratio (SNR) can be used
to yield a winning solution [2]. Based on these criteria,
several adaptive algorithms have been proposed. Smart
antenna can be used at both base station and mobile stations
to achieve transmit and receive diversity. Receive diversity
uses one or more antenna at the receiver to dynamically
combine the received signals. This does not demand more
power compared to the conventional antenna. Use of a smart
antenna at mobile station is not practical. It increases the
weight and power consumption of the mobile and the cost
[3]. Therefore, we only consider a smart antenna at the base
station on the reverse link.
V. RESULTS
We use 900 to 2100 MHz beam forming for 3g smart
antenna system and provide simulation results from these
matlab 7.8. We also demonstrate the results with the
conventional single-element antenna. We also examine the
effects of different design parameters in smart antenna
system performance.

Dubey, Raikwar and Tiwari 73
VI. CONCLUSION
We study the smart antenna technologies for gsm
systems. Using Computer simulation, we show that smart
antenna has powerful capabilities to reduce co channel
interference by forming deep nulls in the directions of
interference. We summarize the results of simulation. Smart
antenna (using four to six elements) can provide an average
gain of 6–8 dB as compared to conventional single element
antenna. Smart antenna has best performance with four and
six elements. Six-element system has been proposed for
systems, whereas four-element for the UMTS. Most suitable
spacing for antenna elements is half the wavelength.
However, element spacing of less than a wavelength
increases The user data rate does not affect the performance.
This means the system can accommodate any kind of user,
voice, or data. Adding additional output modules can easily
scale the smart antenna system. The number of elements
does not limit the number of users it can accommodate. The
smart antenna can distinguish different users even if they
are from the same direction. This is achieved by exploring
inherent orthogonality of the Gold code of different users.
The bit error tends to be clustered to some particular user.
That is, when error occurs, most of them usually occur on
one or two users, instead of spreading out over all users.
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Performance Improvement Interfering High-Bit-Rate W-CDMA Third-Generation Smart Antenna systems

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