HYPERSPECTRAL IMAGERY CLASSIFICATION USING TECHNOLOGIES OF COMPUTATIONAL INTELLIGENCE

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A REVIEW PAPER ON PERFORMANCE ANALYSIS OF
MIMO BASED OFDMA SYSTEM UNDER FADING
CHANNEL
D. Lalitha Kumari
Asst. Professor, Dept of ECE
JNTUA College of Engineering, Ananthapuramu
Andhra Pradesh, India
Prof. M. N. Giri Prasad
Professor, Dept of ECE
JNTUA College of Engineering, Ananthapuramu
Andhra Pradesh, India
ABSTRACT
In communication systems, Multiple input Multiple output (MIMO) antenna systems with
orthogonal frequency division multi-ple acess (OFDMA) is the most promising combination of
technologies for high data rate services in next generation wireless networks. In MIMO systems,
multiple antennas are used in both transmitter and receiver to improve the communication
performance, whereas the orthogonal frequency division multiple acess (OFDMA) is a multicarrier
multiple acess method. MIMO-OFDMA is commonly used for communication systems due to its
high transmission rates and robustness against multipath fading. Performance assessment of multi-
cell systems based on these technologies is of crucial importance in the deployment of broadband
wireless standards such as WiMAX and 3GPP, LTE. This paper presents a vivid view of recent
trends and developments in Orthogonal Frequency Division Multiple Access (OFDMA). In this
paper we will discuss the basics of OFDM techniques, role of OFDM in this era, its benefits and
losses and also some of its application. This paper discusses the structure and implementation of
an OFDMA modem employed in wireless communication. Orthogonal rate of recurrence Division
Multiplexing (OFDM) is just about the latest modulation techniques used so that you can combat
the frequency-selectivity from the transmission channels, achieving substantial data rate without
inter-symbol disturbance. In this paper, we present a study of various interference effects in a
MIMO-OFDMA system and their compensation techniques. In this paper, we present a
comprehensive survey on MIMO OFDMA for wireless communications.
Key words: OFDMA, MIMO, CFO, MUI, BER
Cite this Article: D. Lalitha Kumari and Prof. M. N. Giri Prasad, A Review Paper on Performance
Analysis of MIMO Based OFDMA System Under Fading Channel, International Journal of
Electronics and Communication Engineering and Technology, 8(1), 2017, pp. 32–42.
http://www.iaeme.com/IJECET/issues.asp?JType=IJECET&VType=8&IType=1

A Review Paper on Performance Analysis of MIMO Based of DMA System Under Fading Channel
1. INTRODUCTION
The recent very high demand for multimedia services in wireless communication systems requires high
transmission rates. But this will result in frequency selective fading and inter-symbol interference (ISI).
Orthogonal Frequency Division Multiplexing (OFDM) is a communication technology used in WLANS.
The basic idea of OFDM is to divide the available spectrum into several orthogonal sub channels so that
each narrowband sub channel experiences almost flat fading allowing sub channels in the frequency
domain thus increasing the transmission rate [1]-[2]. In OFDMA distinct sub-carriers are assigned to
different users for simultaneous transmission. Multiple users share the bandwidth simultaneously. Hence
the users and the base station in the OFDMA system are required to be synchronous in frequency domain.
Orthogonal frequency division multiple access (OFDMA) has been selected for next generation wireless
systems for its flexibility and scalability.
Scope of the Research: The migration to 4G networks will bring a new level of expectations to wireless
communications. Just as the digital wireless revolution in the 1990s made mobile phones available for
everyone, the higher speeds and packet delivery of 4G networks will make high-quality multimedia
available everywhere. The key to achieving this higher level of service delivery is a new air interface,
OFDMA, which is in turn enabled by the high level of performance offered by a new generation of
advanced DSPs. By dividing a given amount of spectrum into numerous small subcarriers, OFDMA
provides a robust signal that requires relatively little power yet uses bandwidth very efficiently. Carriers
will benefit from greater flexibility by using OFDMA, since in the same spectrum they will be able to offer
more channels, including higher-bandwidth channels, with more types of services. Currently these systems
are still being defined and prototyped but significant pieces of 4G technology are already in place and the
changeover to a new level of expectations in wireless communications will be underway soon.
The pushing demand of broadband transmission for high throughput services makes OFDMA with
multi-antenna (or multiple input multiple output - MIMO) systems the most versatile and efficient solution
for high spectral efficiency requirements. Nowadays, different commercial solutions employing the
OFDMA technology are under development, such as the IEEE Worldwide Interoperability for Microwave
Access (WiMAX) [1] and the Long Term Evolution (LTE) of the 3rd Generation Partnership Project
(3GPP) [2]. These high data-rate systems are expected to operate in heterogeneous environments. The
historical roadmap dates back from the early 60’s with the idea of using frequency division multiplexing
with overlapping sub channels performance of an efficient parallel data transmission system to refer the
OFDM system. During the 80’s, 90’s coded OFDM (COFDM) has been investigated for digital video
broadcasting (DVB) as first application. OFDM is an efficient modulation scheme for broadband wireless
communications. OFDM is being commercially applied for wireless local area networks. The advantage of
OFDM includes robustness and high spectral efficiency. The pushing demand of broad band transmission
for high throughput services makes OFDMA with multi antenna (or MIMO) systems. The paper is
organized as follows: We start with basic discrete system model in Section II. We present a discussion on
Performance Analysis of MIMO based OFDMA System under Fading Channel in Section III. We present
literature survey on Performance Analysis of MIMO based OFDMA System in Section IV. Conclusion and
future extensions are given in Section V.
2. SYSTEM MODEL
Basic System model for uplink OFDMA
We consider an uplink OFDMA system with K users as shown in Fig. 1, where each user communicates
with a BS through an independent multipath channel. We assume that there are N subcarriers in each
OFDM symbol and one subcarrier can be allocated to only one user. The information symbol for the uth
user on the kth subcarrier is denoted by ,u
k uX k S∈ , where uS is the set of subcarriers assigned to the thu

D. Lalitha Kumari and Prof. M. N. Giri Prasad
user, and
2
E 1u
kX  =
  
. Where [ ]E . denotes the expectation operator. Then { }1 0,1,..., 1K
u uS N= = −U and
u vS S for u vφ= ≠I . 2
.nσ
Figure 1 Basic System model for uplink OFDMA.
The length of the cyclic prefix added is gN sampling periods, and is assumed to be longer than the
maximum channel delay spread, L − 1, normalized by the sampling period (i.e., 1gN L≥ − ). After IDFT
processing and cyclic prefix insertion at the transmitter, the time-domain sequence of the user, u
nx , is given
by
2
1
, 1
u
j nk
u u N
n k g
k S
x X e N n N
N
π
∈
= − ≤ ≤ −∑
1
The uth user’s signal at the receiver input, after passing through the channel, in the case of perfect
synchronization, is given by
*u u u
n n ns x h=
2
where * denotes linear convolution and u
nh is the uth user’s channel impulse response. It is assumed
that u
nh is non-zero only for n = 0,...,L−1, and that all users’ channels are statistically independent. We
assume that u
nh ’s are i.i.d. complex Gaussian with zero mean and ( ) ( )
2 2
, ,E E 1/ 2u u
n I n Qh h L   = =
      
, where
,
u
n Ih and ,
u
n Qh are the real and imaginary parts of u
nh . The channel coefficient in frequency-domain, u
kH , is
given by
21
2
0
, 1
j nkL
u u uN
k n k
n
H h e E H
π−−
=
 = =  ∑
3
Let , 1,2,...,u u Kε = denote uth user’s residual CFO normalized by the subcarrier spacing,
0.5,u uε ≤ ∀ and let , 1,2,...,u u Kµ = denote uth user’s residual TO in number of sampling periods at the
receiver. The DFT output on the kth
carrier of the uth user at the receiver in the presence of CFOs and TOs
can be written in the form

, ,
, , ,
self interference (SI)
, ,
, ,
1
u u
u
u
u u u u u u I u I
k k k k k q q k q q
q S q S
q k
K
v v v I v I u
k q q k q q k
v q S
v u
MUI
Y H X H X H X
H X H X Z
∈ ∈
≠
= ∈
≠
= + +
+ + +
∑ ∑
∑ ∑
14444244443
14444244443
4
where u
qX and ,u I
qX are the symbols from the current and interfering frames, respectively, of uth user,
u
kZ is the output noise of variance 2
nσ respectively. So, computation of the exact BER would involve M-fold
integral in the case of the system with only CFOs and 2M-fold integral for the system with both CFOs and
TOs (where M is the number of subcarriers allotted to each user). To reduce this computational
complexity, we adopt an approximate method to compute the BER (involving only a single integral), as
outlined in the next section.
3. MIMO OFDMA
When generated OFDM signal is transmitted through a number of antennas in order to achieve diversity or
cap any gain (higher transmission rate) then it is known as MIMO-OFDM as shown in figure 1. The
different types of multiplexing techniques are shown in figure 2 where the MIMO-OFDMA is used for the
4G mobile communication systems. In this thesis, I will explore the performance of MIMO-OFDMA
system under fading environment. Like any other communication system MIMO-OFDM system also has
transmitter and receiver but the antennas are more than one both at transmit and receive end. MIMO
system can be implemented in various ways, if we need to take the diversity advantage to combat fading
then we need to send the same signals through various MIMO antennas and at the receiving end all the
signals received by MIMO antennas will receive the same signals traveled through various path. In this
case the entire received signal must pass through un-correlated channels. If we are inserted to use MIMO
for capacity increase then we can send different set of data (not the same set of data like diversity MIMO)
via a number of antennas and the same number of antennas will receive the signals in the receiving end.
For MIMO to be efficient antenna spacing need to be done very carefully- at least half the wave length of
the transmitting signal.
OFDMA is a multiple-access scheme that has characteristics of OFDM and frequency-division
multiple access. OFDM transmits data from one user within a time slot, whereas OFDMA simultaneously
transmits data for MUs. Inherited from OFDM, OFDMA is also immune against multipath and has other
favorable characteristics. OFDMA was proposed for several broadband wireless systems such as the LTE
downlink of cellular systems [4], IEEE802.16 standards for wireless metropolitan area network, and digital
video broadcasting return channel terrestrial [5].In OFDMA, the entire bandwidth is divided into a number
of subchannels for parallel transmission of symbols from different users. How to assign subchannels to
each user is an important issue and greatly influences the system performance. There are two schemes of
the carrier assignment scheme: 1) the subband scheme and 2) the interleaved scheme. In the subband
scheme, a group of adjacent subchannels is allocated to the same user, whereas in the interleaved scheme,
uniformly spaced subchannels are allocated to the same user. The interleaved scheme can obtain frequency
diversity, whereas channel estimation is easier for the subband scheme. Therefore, the interleaved scheme
is preferable for fixed wireless communications, where channel slowly changes with time and channel
estimation is easier than fast time-varying channels. The synchronization issues of OFDMA are
summarized in [6]. For wireless links with channel feedback for adaptive subchannel/subcarrier allocation,
the subband scheme requires less information for channel feedback [7], [8]. By adaptive subchannel
allocation in OFDMA, the system capacity can significantly be increased, as shown in [9] and [10]. The
performance of uplink OFDMA depends to a large extent on how well the orthogonality among different

subcarriers is maintained at the receiver. Factors including i) timing offsets (TO) of different users caused
due to path delay differences between different users and imperfect timing synchronization, and ii) carrier
frequency offsets (CFO) of different users induced by Doppler effects and/or poor oscillator alignments,
can destroy the orthogonality among subcarriers at the receiver and cause multiuser interference (MUI).
MIMO OFDMA system is used for high data rate services in wireless communication networks. Here
multi cell systems developed based on broadband wireless standards such as Wi-MAX and 3GPP LTE.
Beam forming systems with Coordinated Scenario is used for mitigation of stationary interference and
Diversity model with Randomized interference is used for Non-Stationary interference. The analytical
frame work for bit error probability of multi cell bit interleaved convolution coded system. With improved
multimedia services such as video conferencing, video streaming the demand for data rate communication
with a high speed has been increased. MIMO-OFDMA fulfils that demand. But there is a problem of
resource allocation in MIMO-OFDMA. The user needs different power levels when he moves from and to
the antenna. For example, less power is required for the user when he is near to the antenna and large
power is required for him when he is far from the antenna. But the problem is the user has been allocated a
fixed power level which doesn’t vary with respect to the distance. Moreover a user with high transmitting
power is the only one detected at the base station and no user with low transmitting power is detected.
Performance of the cellular system can be enhanced with the power control strategy. Hence power control
has significant effect on both performance and capacity. In cellular system downlink power control was far
less sophisticated than uplink power control, resulting in the downlink capacity being more constrained
than uplink [9,10]. Depending on propagation conditions, the mobile may receive a power control
command that indicates at what power level the mobile should transmit. However, the losses of uplink and
downlink are unsymmetric because Rayleigh fading is frequency selective. To overcome this, a closed-
loop power control is required to vary the transmitted power by the mobile. With this it can receive an
equal Eb/N0 from all mobiles. However, with the existence of a multipath fading environment, it is useful
to add another power control mechanism to adjust the desired Eb/N0 level according to the mobile's error
rate measured at the bases station- this is known as the outer loop power control.
Uplink power control serves the following functions:
• It equals the received power level from all mobiles at the base station. This function is important for system
operation. The better the power control, the greater the Reduction in cochannel Interference and thus,
increase in capacity.
• It also reduces the necessary transmission power level to achieve better quality of service. This minimizes
the cochannel interference, which improves the system capacity.
Uplink power control achieves the above functions through the following mechanisms: 1.Open loop
power control, and 2. Closed loop power control, which can be sub-divided into-
(a) closed outer loop power control, and (b) closed inner loop power control.
The combination of STBC with OFDM, termed ‘STBC-OFDM’ was first proposed by Agrawal in [1].
Following this development, various researchers have focused on designs for scenarios where the channel
is assumed to be known at the receiver, for example, the designs proposed in [2]. Which indicate that the
combination of MIMO techniques with OFDM [3] improves the transmission rate, range and reliability.
B. Generalized STBC System Model
STBC is regarded as the generalization of the Alamouti coding. Tarokh et al generalized STBC to an
arbitrary number of transmit and receive antennas in [3]. STBC can achieve full rate and full diversity
which as stated earlier is specified by the number of different symbols to transmit and the number of time
slots required to transmit the entire STBC block. In addition, STBC allows very simple decoding algorithm
based on the ML decoding described in the previous Subsection. Fig (1) shows a block diagram of the
generalised STBC communication link. Like Alamouti case, data is first mapped by a 2k
points modulator
resulting in ns data symbols passed to the STBC encoder. At the receiver, the data is decoded with the

STBC decoder that contains channel estimator, combiner and ML detector. Based on the type of
modulation used, STBC uses either real or complex constellation. STBC with real constellation is Pulse
Amplitude Modulation (PAM) or Binary Phase Shift Keying (BPSK) signal, and with complex-
constellation is M-PSK or M-QAM signal.
Performance of Space-time Block Codes
The performance of space-time block codes depends on the type of modulation and the number of transmit
and receive antennas used. Complex modulations give better bit-error-rate performance than real
modulations and it is especially true when the number of transmit antennas is more than 1. However,
space-time block code with real modulation would have better bandwidth efficiency performance than
complex modulation. This is because space-time block codes with real modulation require transmitting less
data than space-time block codes with complex modulation. On the other hand, space-time block codes
with two number of transmit antennas always give better performance because it transmits more data. This
would give the receiver the ability to recover the transmitted data. Moreover, with larger number of receive
antennas, the same transmitted data would be received by more than one receive antenna. This is an
advantage because if one receive antenna did not recover the transmitted data correctly, the second receive
antenna could. The chance that at least one out of two receive antennas would receive the transmitted data
uncorrupted is always higher than if there is only one receive antenna.
C. Receiver structures
MIMO with Zero Forcing Equalization
The zero forcing approach tries to find a matrix U which satisfies UH=I. The Zero Forcing (ZF) linear
detector for meeting this constraint is given by,
( )
1H H
U H H H
−
=
MIMO with MMSE Equalization
The Minimum Mean Square Error (MMSE)
Approach tries to find a coefficient W which minimizes the criterion,
{ }[ ][ ]H
y yE U x U x− −
.
Solving,
1
0
H H
U H H N I H
−
 = + 
Using the Minimum Mean Square Error (MMSE) equalization, the estimate of the two transmitted symbols
s1 and s2 is given by
( )
11 1
0
2 2
H H
s y
H H N I H
s y
−   
= +   
   
)
)
Zero Forcing Equalization with Successive Interference Cancellation
Using the Zero Forcing (ZF) equalization approach described above, the estimate of the two transmitted
symbols s1 and s2 is obtained as
( )
11 1
2 2
ˆ
ˆ
H H
s y
H H H
s y
−   
=   
   

Taking one of the estimated symbols (for example 2
ˆs ) and subtracting its effect from the received vector y1
and y2 we get
1 1,2 2 1,1 1 11
2 2,2 2 2,1 1 22
ˆ
ˆ
y h s h s wr
y h s h s wr
− +    
= =     − +      .
Expressing in matrix notation,
1,11 1
1
2,12 2
hr w
s
hr w
    
= +    
    
r=h s1 +w
The equalized symbol is,
1
ˆ
H
H
h r
s
h h
=
Following Similar procedure the estimate of s2 is also obtained.
Zero Forcing Equalization with Optimally ordered Successive Interference Cancellation
In classical Successive Interference Cancellation, the receiver arbitrarily takes one of the estimated symbols,
and subtract its effect from the received symbol y1 and y2 . However, we can intelligently choose the order
in which the symbol effect is subtracted. This is done based on the powers of the received symbols in
which the symbol with higher power is subtracted first and then the other [7][8].
The received power at the both the antennas corresponding to the transmitted symbol s1 is,
2 2
1 1,1 2,1Ps h h= + .
The received power at the both the antennas corresponding to the transmitted symbol s2 is,
2 2
2 1,2 2,2Ps h h= +
If 1 2Ps Ps> then the receiver decides to remove the effect of 1
ˆs from the received vector y1 and y2 and
then re-estimate 2
ˆs . Else the receiver decides to subtract effect of 2
ˆs from the received vector y1 and y2, and
then re-estimate 1
ˆs .
MMSE equalization with optimaly ordered
Using the Minimum Mean Square Error (MMSE) equalization, the receiver can obtain an estimate of the two
transmitted symbols s1, s2, given by
11 1
0
2 2
ˆ
ˆ
H
s y
H H N I
s y
−   
 = +    
   
If 1 2Ps Ps> then the receiver decides to remove the effect of 1
ˆs from the received vector y1 and y2 and
then re-estimate 2
ˆs . Else the receiver decides to subtract effect of 2
ˆs from the received vector y1 and y2, and
then re-estimate 1
ˆs .
MIMO with ML equalization
The Maximum Likelihood receiver tries to find ˆs which minimizes,
2
ˆJ y Hs= −

2
1.1 1.21 1
2.1 2,22 2
ˆ
ˆ
h hy s
J
h hy s
    
= −     
    
Since the modulation is BPSK, the possible values of s1 and s2 are +1 or -1. So, to find the Maximum
Likelihood solution, we need to find the minimum from the all four combinations of s1 and s2.
D. Observations
The BER performance of 2 transmit 2 receive alamouti case is much better than 1 transmit 2 receive MRC
case. This is because the effective channel concatenating the information from 2 receive antennas over two
symbols results in a diversity order of 4. The improvement is brought in, because decoding of the
information from the first spatial dimension (s1) has a lower error probability than that of the symbol
transmitted from the second dimension. The BER curve with ZF equalization for 2×2 MIMO channel is
identical to BER plot for 1 transmit 1 receive system. The Zero Forcing equalizer is not the best possible
way to equalize the received symbol. The zero forcing equalizer helps us to achieve the data rate gain, but
can not take the advantage of diversity gain (as we have two receive antennas). Compared to the case of
Zero Forcing equalization alone, addition of successive interference cancellation results in around 2.2dB of
improvement for BER of 10-3
. Ordered variant of successive interference cancellation shows better
performance than the simple successive interference cancellation. Compared to the case of Zero Forcing
equalizer , at BER of 10-3
, it can be seen that the Minimum Mean Square Error (MMSE) equalizer
results in around 3dB of improvement. Successive interference cancellation with optimal ordering
improves the performance with Zero Forcing equalization. Compared to the case of Minimum Mean
Square Equalization with simple successive interference cancellation, addition of optimal ordering results
in around 5.0dB of improvement for BER of 10-3
. MMSE equalization with ordered successive
interference cancellation provides performance which is slightly poorer than ML shown in figure 2.
Many simulations have been done on the performance of different space-time block codes using
different types of modulation schemes and different numbers of transmit and receive antennas. In our
simulation on the different implementations of space-time block codes, the channel coefficients are always
assumed flat Rayleigh. From the figure 3, we can see the performance of space-time block codes using
QPSK, and BPSK modulation schemes. The bit-error-rate performance of the system using BPSK
modulation is better than the performance of space-time block codes using QPSK modulation by
approximately 3~4 dB. The performance of space-time block codes using two transmitters and two
receivers shows better than the performance of space-time block codes using two transmitters and one
receiver antenna by approximately 7~8 dB.
Figure 2 BER comparative plots for 2×2 MIMO channe
0 5 10 15 20 25
10
-5
10
-4
10
-3
10
-2
10
-1
Average Eb/No,dB
BitErrorRate
BER for BPSK modulation with 2x2 MIMO and MMSE-SIC equalizer (Rayleigh channel)
theory (nTx=2,nRx=2, ZF)
theory (nTx=1,nRx=2, MRC)
sim (nTx=2, nRx=2, MMSE-SIC)
sim (nTx=2, nRx=2, MMSE-SIC-Sort)
0 5 10 15 20 25
10
-5
10
-4
10
-3
10
-2
10
-1
Average Eb/No,dB
BitErrorRate
BER for BPSK modulation with 2x2 MIMO and ML equalizer (Rayleigh channel)
theory (nTx=1,nRx=1)
theory (nTx=1,nRx=2, MRC)
sim (nTx=2, nRx=2, ML)

Figure 3 BER Plot for STBC-OFDM BPSK/4-QAM/16-QAM (2X1 vs. 2X2) (Alamouti)
4. LITERATURE REVIEW
This section describes the brief out line of the literature survey related to the 4th
generation wireless
technology.
Year Author Title Approach Result Result
Conference on
Universal Personal
Communications
ICUPC '95, Nov. 1995.
Beek, J.J. van de, M.
Sandell, M. Isaksson,
and P.O. Borjesson
Low- Complex
Frame
Synchronization
in OFDM
Systems
Proposed a frame synchronization
algorithm using the repetition in the
OFDM symbol due to the CP to
estimate the frequency offset.
The limits of the use of the CP for
synchronization and the use of the virtual
subcarriers for the synchronization of an
OFDM system is proposed.
in Proceedings of the
45th IEEE Vehicular
Technology
Conference (VTC '95),
Vol. 2, Chicago, Ill,
USA, July 1995 pp.
815–819.
J.-J. van de Beek, O.
Edfors, M. Sandell,
S. K. Wilson, and P.
O. Börjesson,
On channel
estimation in
OFDM systems
Proposed the linear minimum mean
square error (LMMSE) channel
estimation method based on channel
autocorrelation matrix in frequency
domain . To reduce the
computational complexity of
LMMSE estimation, a low-rank
approximation to LMMSE
estimation has been proposed by
singular value decomposition
The drawback of LMMSE channel estimation
is that it requires the knowledge f channel
autocorrelation matrix in frequency domain and
the signal to noise ratio (SNR) Though the
system can be designed for fixed SNR and
channel frequency autocorrelation matrix, the
performance of the OFDM system gets
degraded significantly due to the mismatch of
estimated parameters with system parameters.
Proc.IEEE
GLOBECOM 04, pp.
2808-2812,
December2004.
R. Fantacci, D.
Marabissi, and S.
Papini,
Multiuser
interference
cancellation
receivers for
OFDMA uplink
communications
with carrier
frequency offset
A two-stage equalizer was proposed
in this paper to suppress ICI and
MUI in a downlink multiuser
OFDM system. The equalizer
overcomes BER degradation due to
frequency offsets. A method to
counteract the effect of different
FOs among the users in an uplink
OFDMA system with frequency
selective Rayleigh fading channel .
The interference was reduced by reconstruction
and removal of interfering signals in the
frequency domain using selective cancellation
method. The performance of cancellation
schemes was evaluated by assuming a FO
estimate. An OFDMA framework for arbitrary
subcarrier assignment was suggested in this
work. The received signals were constructed in
the frequency domain that would be received if
there were no frequency synchronization errors.
IEEE Transactions on
Vehicular Technology,
Vol. 57, No. 6, Nov.
2008, pp. 3462-3470.
J. Chen, Y.C. Wu, S.
C. Chan and T. Sang
Ng,
Joint Maximum-
Likelihood CFO
and Channel
Estimation for
OFDMA Uplink
using Importance
Sampling
Derived both CFO estimator and
channel estimator.
An optimization theorem was used to propose a
method for estimation to overcome the
complexities faced due to direct
implementation of the estimator. The proposed
estimator provides optimal solution even
without the initial estimate.
IEEE Transactions on
Wireless
Communications , vol.
6, No.7, pp. 2560-
2571, July 2007.
S. Manohar, V.
Tikiya, D. Sreedhar,
and A.
Chockalingam
Cancellation of
Multiuser
Interference due
to Carrier
Frequency
Offsets in Uplink
OFDMA
This scheme ﬁrst performs CFO
compensation (in time domain)
followed by K DFT operations
(where K is the number of users)
and multi-stage LPIC on these DFT
outputs.
The performance and complexity comparison
of this scheme (1) with the scheme (2)
proposed by Huang and Letaief which performs
CFO compensation and interference
cancellation in frequency domain using
Circular Convolution (HLCC scheme) are
compared. This scheme performs better than
the scheme proposed by Huang and Letaief
when the individual CFO values are small,
whereas (2) scheme performs better than the
HLCC scheme (1) when the CFO differences
are small (even if the individual CFO values are
0 5 10 15 20 25
10
-6
10
-5
10
-4
10
-3
10
-2
10
-1
10
0BER for BPSK/4-QAM/16-QAM with Alamouti(2X1)and (2X2) STBC-OFDM (Rayleigh channel)
SNR(dB) ---->
SymbolErrorRate---->
sim (nTx=2, nRx=1, Alamouti(BPSK))
sim (nTx=2, nRx=1, Alamouti(4-QAM))
sim (nTx=2, nRx=2, Alamouti(BPSK))

Year Author Title Approach Result Result
large).
IEEE Trans. Veh.
Technol., Vol. 56, No.
4, Jul. 2007, pp. 1892–
1895.
G. Ren, Y. Chang, H.
Zhang, and H. Zhang
An Efficient
Frequency Offset
Estimation
Method with a
Large Range for
Wireless OFDM
Systems
Analyzed the algorithm presented
by Boumard and came to the
conclusion that the performance of
this algorithm depends highly on the
frequency selectivity of the channel.
They proposed an improved version of
Boumards algorithm to solve that problem. The
authors also present several simulations that
seem to confirm that fact.
IEEE Trans.
Commun., Vol. 58,
Sep. 2010, pp. 2486–
2492.
G. A. Ropokis, A. A.
Rontogiannis, P. T.
Mathiopoulos, and
K. Berberidis,
An Exact
Performance
Analysis of
MRC/OSTBC
over Generalized
Fading
Channels,”
Described a unified framework to
accurately compute a set of
performance figures over
generalized fading channels
(information outage probability,
ergodic capacity, average symbol
and bit error probability).
This includes the sum of independent but not
necessarily identical variances following
Nakagami- , Rice, Hoyt, Beckmann and
Shadowed Rice distributions, with maximum
ratio combining (MRC) or orthogonal space-
time block coding (OSTBC) as diversity
schemes.
IEEE Trans. Signal
Processing (2003)
51(6): 1615–1624.
Barhumi I, Leus G &
Moonen M
Optimal Training
Design for
MIMO–OFDM
Systems in
Mobile Wireless
Channels
Presented the optimal pilot sequence
in MIMO-OFDM system, which
should be equi-spaced, equi-
powered and phase shift orthogonal
in order to obtain the minimum
mean square error (MSE) of the
least squares (LS) channel estimate..
The channel impulse response is assumed to be
random in the MMSE estimation where the
SNR and prior information on the channel are
exploited. The recursive least square (RLS)
algorithm can be used to enhance the channel
estimation performance, but it is most suitable
for slow fading channels.
5. CONCLUSION AND FUTURE SCOPE
In this paper we have briefly described OFDM, OFDMA and MIMO for wireless communications. We
introduced related modulation scheme OFDM and access schemes OFDMA. We also summarized the
types of MIMO for modulation and access schemes. MIMO OFDMA technique to improve the capacity
and diversity in cellular systems. This paper reviews based on literature study that OFDMA is much better
suited to a multipath channel than the usual single carrier transmission method such as16-QAM. The desire
for high data rate wireless communication have been increasing drastically throughout the last decade. This
paper has looked into the role of MIMO- OFDMA within the wireless communication and it is advantages
over single provider transmission. There are also a few limitations of this technique which is often
removed with the guide of suitable techniques.
In this review paper, we presented a study of various interference effects in a MIMO-OFDM system
and their compensation techniques. Among the techniques discussed, most of them use estimation and
compensation process using either pilot symbols or preamble. The different forms of interferences have
been studied extensively individually but joint consideration of all interferences has not been studied so far
in literature. Hence an efficient algorithm has to be framed which takes into account all the interferences
and is a part of our future work.
REFERENCES
[1] R. Fantacci, D. Marabissi, and S. Papini, Multiuser interference cancellation receivers for OFDMA
uplink communications with carrier frequency offset, Proc. IEEE GLOBECOM04, pp. 2808-2812,
December2004.
[2] A.Rajeswari, Fuzzy based adaptive weighted multiuser Interference cancellation in OFDMA systems,
IJAST, 26(8), pp. 47-55, Jan 2011.
[3] D. Huang and K. B. Letaief, An interference cancellation scheme for carrier frequency offsets correction
in OFDMA systems, IEEE Trans. Commun., 53(7), pp. 1155-1165, July 2005.
[4] Manohar, Shamaiah and Sreedhar, Dheeraj and Tikiya, Vibhor and Chockalingam, Cancellation of
Multiuser Interference Due to Carrier Frequency Offsets in Uplink OFDMA, IEEE Transactions on
Wireless Communications , 6(7), pp. 2560–2571, July 2007.
[5] K. Raghunath and A. Chockalingam, SIR analysis and interference cancellation in uplink OFDMA with
large carrier frequency and timing offsets, IEEE Trans. Wireless Commun., 8(5), 2202-2208, May 2009.

[6] M. Morelli, C-C.J. Kuo, and M,-O. Pun, Synchronization techniques for orthogonal frequency division
multiple access (OFDMA): A tutorial review, Proc. IEEE, 95(7), pp.1394–1427, Jul. 2007
[7] P. Viswanath, D.N.C. Tse, and R.L.Laroia, Opportunistic beam-forming using dumb antennas, IEEE
Trans. Inf. Theory, 48(6), pp.1277-1294, Jun. 2002.
[8] NiveditaChourasia and SopanKhadkodkar, MIMO Communication Study and Relay Diversity Analysis
in Wireless Communication, International Journal of Electronics and Communication Engineering and
Technology, 7(6), 2016, pp. 56–64
[9] Koiloth S R S Jyothsna and Tummala Aravinda Babu. Performance Analysis of Clipped STBC Coded
MIMO OFDM System. International Journal of Electronics and Communication Engineering &
Technology, 7(1), 2016, pp. 28-44.
[10] Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy, Design, Implementation, and
Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System.
International Journal of Electrical Engineering & Technology, 7(3), 2016, pp. 126–144
[11] P. Svedman, S.K. Wilson, L.J.Cimini, and B.Ottersten, A Simplified opportunistic feedback and
scheduling scheme for OFDM, in Proc. IEEE Veh. Technol. Conf, May 2004. 4, pp. 1878–1882.
[12] V. Tarokh, N. Seshadri and A.R. Calderbank, Space-time codes for high data rate wireless
communications: Performance criteria and code construction, IEEE Trans. Inf. Theory, 44(2). pp. 744-
764, Mar. 1998
[13] A. Wittneben, A new bandwidth efficient transmit antenna modulation diversity scheme for linear
digital modulation: in Proc. IEEE Conf. Commun , 1993

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