An Efficient Performance of Mimo - Ofdm Based Cognitieve Radio System for Arrays Signal Processing

IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 7, Issue 3 (Sep. - Oct. 2013), PP 52-55
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An Efficient Performance of Mimo - Ofdm Based Cognitieve
Radio System for Arrays Signal Processing
Alaa Desher Farhood[1]
, Maham Kamilnaji[2]
1, 2(Technical Trainer, Foundation Of Technical Education, Iraq, Baghdad.).
.
Abstract: The wireless channel is central within this context, thus estimating the channel is the key to make CR
operational, taking in consideration that the transmission-reception technology is available. In this thesis, we
design a MIMO system using OFDM modulation technology to transmit and receive two signals over the mobile
wireless channel. First formulate the pilot design as a new optimization problem. We use MIMO concept to
enhance system capacity and robustness of the wireless transmission. In Multi-Input Multi-Output (MIMO)
based cognitive radio (CR) systems, with the increasing demand for data rate and reliability in Wireless
communicationsand devices, several issues become very important like bandwidth efficiency, quality of service
and radio coverage. In this new scheme, adaptive arrays are group-selected in the spatial domain. Simulation
shows that the proposed system can get significant performance improvements over the conventional array
based OFDM systems over frequency-selective multipath fading channels with cognitive radio (CR) system.
Keywords: Cognitive radios, MIMO, OFDM, and Joint transmit and receive group selected arrays.
I. Introduction
The use of the radio spectrum is regulated by governmental rules. Almost allparts of the radio spectrum
are licensed today. The FCC published a thesis in [1] where the spectrum use in the United States is presented in
the aim of betterspectrum utilization.On the other hand, studies have shownthat major licensed bands are largely
underutilized. This hasinitiated the idea of cognitive radio (CR), where secondaryusers are allowed to utilize the
licensed bands without causing armful interference to the licensed or primary users [2].The CR technology has
the potential to significantly increasethe efficiency of the spectrum utilization while maintaining the QoS
requirement of the primary users. Multi-Input Multi-Output (MIMO) is the most widelyused technology in
current and future wireless communicationsystems. Since OFDM can naturally provide flexibility to fill
inspectrum holes over a wide bandwidth, it has been considered as one of the best candidates for the physical
layer of CR systems [3], [4].The channel state information (CSI) is crucial for CR systems [5].
In practice, the CSI can be estimated by using pilot tones. The selection of pilot tones will significantly
affect the channel estimation performance. For conventional OFDM systems where all subcarriers can be used
for transmission, the issue of pilot design has been well studied [6]. However, these methods are not effective
for OFDM-based CR systems, since the subcarriers used by the primary users cannot be employed by the
secondary users. Hence the available subcarriers for the secondary users may be non-contiguous which brings
new challenges to pilot design.
The simplest pilot design for OFDM-based CR system is to predesign pilot tones for the conventional
OFDM system and then deactivates those tones already used by the primaryusers according to the result of
spectrum sensing. At the receiver, the remaining activated pilot tones are used forchannel estimation. Directly
implementing channel estimationbased on such pilot pattern leads to poor performance [7].To obtains
satisfactory channel estimation performance, a shift pilot scheme is proposed in [7]. After predesigning pilot
tonesand deactivating some of them according to the spectrumsensing result, this scheme selects some activated
data subcarriersas the new pilot tones. Specifically, when a pilot toneis deactivated, its nearest activated data
subcarrier is then usedas the new pilot tone. The similar idea can be also found in [8], [9]. Although such a
scheme outperforms the aforementioned done, it cannot provide satisfactory performance in all cases.This is
mainly because the positions of pilot tones are notoptimized.
In this thesis, we propose a new practical pilot design method for MIMO-based CR systems. Unlike the
above twoschemes, the proposed one does not predesign the pilot tones.The pilot design is performed after
spectrum sensing (i.e.,after the set of activated subcarriers is obtained). To obtaina low-complexity method, we
first formulate the pilot designin terms of a new optimization problem. Instead of minimizingthe mean-square
error (MSE) of the least-squares (LS) channelestimator itself, we minimize an upper bound which is relatedto
this MSE. The MSE and BER are used as measure in judging the performance of each pilotpattern. According to
that performance of pilot pattern has beenimplemented in MIMO based CR system. Virtual pilot concept has
beenreviewed and implemented in all proposed pilot patterns. The advantages anddisadvantages of virtual pilot
concept has been discussed through our simulationresults analysis. Performance of the system while a licensed
user is occupying asub-band of the spectrum and the effect of the licensed user on the pilot patternhas been

An Efficient Performance Of Mimo - Ofdm Based Cognitieve Radio System For Arrays Signal
studied. Spectrum overlying according to licensed user activity has beensimulated. The three proposed pilot
patterns were implemented in the MIMOsystem and their performances were tested. The decision of the best
performingpattern is made by observing the MSE and BER obtained from the simulation resultsin the case
where no LU is active. The hexagonal pattern shows the lowestMSE and BER compared to the other two
patterns. Hence the hexagonal pattern is chosen to be implemented in the CR system.
II. System Design Model
A. MIMO model with CR system
The MIMO-based CR system under consideration is shownin Fig.1. The pilots are designed according
to the result ofthe spectrum sensing. After subcarrier assignment where thesubcarriers occupied by the primary
users are deactivated,pilot symbols are inserted and the data are modulated onthe remaining activated
subcarriers.We employ the MIMO concept in our simulation platform because it has beenproven that MIMO
can achieve a major breakthrough in providing reliable wirelesscommunication links. This reliability is in the
context of the channel estimationin our case. With the MIMO concept we improve the bitrate and BER of
theoverall system. MIMO is capable of this improvement, because of the property ofmultiple transmission
multiple reception. This property is a form of spatial diversity.This diversity is the most effective technique to
accomplish reliable communicationover the wireless channel and combating with fading, because it providesthe
receiver with multiple copies of the transmitted signal. Those multiple copiesare independently faded. If at least
one copy of the transmitted signal is receivedcorrectly, we will have the transmitted signal back. This property
improves theBER significantly (low BER) as shown in chapter 6. Beside this, MIMO increases thechannel
capacity also, which means more throughputs. There are different ways toexploit multiple antennas at both sides
of the communication channel. To improvethe transmission reliability, the transmit antennas should be used
such that transmitdiversity is achieved. The transmission rate is comparable to the one obtainedin SISO. To
improve the transmission rate, independent signals are transmittedfrom the different transmit antennas. i.e. there
is no correlation between the transmittedsignals from the different antennas. In this case the reliability is not
muchimproved [10].
   
     
1
,
0 1
K M
k
r i r i r
k i
y t h t s t k n t

 
   (1)
Where si(t) is the transmitted signal from antenna i at time t, h(k) i,r(t) is the channel coefficient for the kth path
from transmit antenna i to receive antenna r at time t. nr(t) is the Additive White Gaussian Noise. For mobile
communications, the channel tap coefficients are random variables. In case the wireless channel varies very
slowly, the tap coefficients remain constant for each frame of data. For Rayleigh fading channels, the channel
tap coefficients are modeled as complex Gaussian random variables which have zero mean. The different
channel taps are assumed to be independent. The average channel gains for different paths are determined from
the power Delay profile of the wireless channel. In this work we assume that the channel tap powers decays
exponentially. Hence we use the exponential power delay profile. If the MIMO-OFDM system has Nc
subcarriers and the fading coefficients are spatially uncorrelated and that the fading coefficients remain constant
during one OFDM symbol. Then the transmitted signal over M antennas can be represented by a matrix
XOFDM with dimensions NcxM. A symbol transmitted at subcarrier n on transmit antenna is xi (n).
Figure 1: MIMO setup
B. MIMO/OFDM system Model group selection array signal processing
The communication system model is plotted in Fig. 2.Communication system side at transmitter is
equipped withM transmitting antenna arrays and communication systemside at receiver is equipped with K
receiving antennaarrays. The N signal symbols inputd =[d(0), d(1),d(2),d(N -1)] is converted into parallelsignal
symbols. Group-selected arrays signal processing foreach sub-carrier is performed with antenna arrays

groupselectedfromNPM antenna arrays according to arrays groupselectioncriterion. The inverse fast Fourier
transforms (IFFT)transforms the group-selected signal symbols into the timedomainsamples. Communication
system sides at receiver, out of antenna arrays are group-selected. Then, thegroup-selected signals are
transformed back into thefrequency domain with an FFT. Finally, the signal symboldata originating from the
group-selected transmittingantenna arrays is combined to form the arrays of outputsignals i thdˆ = [dˆ(0),
dˆ(1),d(2)….dˆ(N -1)] .
Assume the wireless communication networks channelbetween and the antenna array at the transmitterand
receiver, respectively, to be characterized by a multipath fading channel. Let the H(i) denoteM x
KdiscretetimeMIMO/OFDM channel matrix on the i subcarrier, Then weighting coefficient vector and receiving
antenna arrays weighting coefficient vector for the sub-carrier as and , respectively. Then, the received signal, ,
for sub-carrier can be written as
where the superscript H denotes the Hermitian operation, the superscript T denotes the transpose operation, is a
Qx1 column vector with the arrays modeled as complex AWGN model with variance 𝜎𝑛
2
is the Px Q group-
selected sub-channel matrix out of Hs(i) M x K channel matrix H(i) and can be rewritten as follows
Then the maximal instantaneous receiving SNR for the ith sub-carrier signal processing can be written as
Where 𝜆 𝑚𝑎𝑥 𝐴 are the largest eigen value of the matrix A.The objective of group selected antenna arrays at
both the transmitter and receiver sides for grouping is to maximize the average SNR. Therefore, the optimal
group selected sub-channel matrix Hs(i) on the ith sub-carrier should maximize the largest eigen value of
channel matrix 𝐻𝑠 (𝑖) ∗
𝐻𝑠 (𝑖) 𝑇
III. Simulation Results
In this section, we give some performance simulation results to evaluate the performance of our
proposed group selected arrays signal processing based new systems. The performance simulation is reached for

MIMO/OFDM communication systems by computer simulation over typical-urban (TU) frequency-selective
Rayleigh fading channels. The RMS delay spread of the 6-ray TU channels we use is 1.0µs , respectively. We
consider MIMO/OFDM communication stems employing QPSK for 128 subcarriers with diff rent antenna
arrays configurations at oth transmitter and receiver sides. We use one OFDM symbol of 148 µs comprised of
one OFDM data symbol duration of 128µs and the cyclic prefix duration of 20 µs .
Add Results Of The Paper 1,2 And 3
Imperfect feedback channel is another case of imperfect channel information. To deeply investigate
how the feedback channel imperfectability affects the performance of the proposed system, we plot in Fig. 4 the
BER performance of the proposed group-selecting approach and conventional antenna arrays without group-
selecting with 2 group-selected transmitting antenna arrays from 3 transmitting and 2 receiving antenna arrays
configuration versus the quality of feedback channel. The SNR was set at 6dB in this figure. As a comparison,
the BER performance of the conventional adaptive antenna array based OFDM system with the same adaptive
antenna arrays configuration at both the transmitter and receiver is also plotted. A close observation of the
results indicates that the proposed approach outperforms the conventional adaptive antenna array based OFDM
system.
IV. Conclusion
In this paper, a new practical pilot design method for MIMO- based CR systems has been proposed.
We have also proposed an efficient sequential method to solve the corresponding optimization problem. The
performance of this pattern converges at almost 20dB SNR for different filter lengths. Above this SNR value
increasing the filter size has no influence any more on improving the performance. Adaptive antenna arrays can
promise to achieve significant increases in a system’s capacity and bandwidth efficiency as well as in QoS
improvement in wireless communications, but they are characterized by a relatively higher implementation
complexity.
References
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