International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
An Efficient LMMSE Estimator for MIMOOFDM systems over Flat Fading Channel
J.Poonguzhali 1 , M.Vadivel 2
1. M.E Scholar, ETCE Department, Sathyabama University
Chennai – 600119, India.
2. Assistant Professor, ETCE Department, Sathyabama University
Chennai – 600119, India.
Abstract --- Multiple Input Multiple Output-Orthogonal
Frequency Division Multiplexing (MIMO-OFDM) systems
play a vital role in the channel estimation. Since all the
performance improvement and increase in channel capacity
are based on accurate channel state information. A Linear
Minimum Mean Square Error (LMMSE) method is proposed
for channel estimation over a flat fading channel. From the
simulation results we can conclude that the proposed
LMMSE algorithm provides better performance in terms of
Mean Square Error (MSE) and Bit Error Rate (BER)
compared to conventional algorithms.
all the frequency components of the signal will experience
same magnitude of fading.
In this paper, we are proposing a LMMSE (Linear
Minimum Mean Square Error) channel estimator [4] over
flat fading channel which can give better performance in
terms of Mean Square Error (MSE) and Bit Error Rate
(BER).
II. SYSTEM MODEL
Keywords - MIMO-OFDM, LMMSE, BER, CP
I. INTRODUCTION
MIMO-OFDM is one of the popular techniques in
wideband digital communication over wire or wireless
media. We may apply it in the areas such as digital
television and audio broadcasting, DSL broadband internet
access and 4G mobile communications. MIMO-OFDM
systems have the advantage of computationally simple
channel estimation [1][6] and equalization. In OFDM the
wideband is partitioned into several narrow band channels
by means of IFFT. OFDM divides the available bandwidth
into many subchannels that are orthogonal to each other.
This principal avoids the interference between the
subcarriers. By adding Cyclic Prefix (CP), Inter-symbol
interference (ISI) on the carrier is removed. This makes
OFDM suitable for wireless communication applications.
OFDM can be combined with Multiple Input
Multiple Output (MIMO) transceiver to increase the
diversity gain as well as system capacity by exploiting
spatial domain. MIMO-OFDM provides numerous parallel
narrowband channels [8] and considered as a new
technology in emerging areas such as 4G.
The main objective of this research work is to
give better performance in terms of Mean Square Error
(MSE), Bit Error Rate (BER) and also to improve channel
capacity and less computational complexity
Dynamic channel estimation [3][5] is necessary
for wideband communication, before demodulating the
signal. A channel estimator is needed to improve the
performance and make full use of subcarrier correlation.
It is performed with the assistance of known
transmitted pilot symbols. Also in order to nullify the
effect of impairments induced by the frequency-selective
fading [2] channel, the performances are improved by
using flat fading channels. The coherence bandwidth of the
channel is larger than the bandwidth of the channel, where
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International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
T
R
P 1
H tr (k)   h tr (p)e
 2 kp 
 j

 K 
- (1)
t 0 r 0 p 0
Where
K - Total number of users
N - Total number of subcarriers
T - Total number of transmitting antennas
R - Total number of receiving antennas
P - Total number of paths in the channel
h tr (p)
- Time domain channel response
IV.PROPOSED ALGORITHMS
Fig.1.System Model of MIMO-OFDM Systems
Figure 1 shows the block diagram of the MIMO OFDM systems. It consists of Mt transmitting antennas
and Mr receiving antennas. Each modulator block in
transmitter side consists of
serial to parallel(S-P)
conversion block, Pilot Insertion block,IFFT block, Add
Cyclic Prefix(CP) block and Parallel to serial Conversion
block followed by transmitting antenna. Each demodulator
block in receiver side consists of serial to parallel(S-P)
conversion block, Remove cyclic Prefix block, FFT block,
Parallel to Serial block followed by channel estimation and
detection block.
In
MIMO-OFDM
systems
perfect
synchronization and Flat fading between transmitter and
receiver is obtained by assuming the assumptions like
channel state, pilot transmission in all the subcarriers.
The channel capacity of the MIMO link can be
increased by the use of spatial multiplexing with assuming
independent data streams in the same time slot.
III. CHANNEL ESTIMATION
The Channel estimation [7] can be done by
combination of frequency domain channel response and
time domain channel response.
We can write the channel estimation
H t r ( k ) problem mathematically by the equation given
below.
The proposed algorithm for channel
estimation provides better performance in terms
of Mean Square Error (MSE) and Bit Error Rate
(BER) compared to conventional algorithms.
A. Proposed channel estimation algorithm
Frequency
domain
channel
response
H tr (k ) can be obtained for k user of t transmitting
antenna and rth receiving antenna by evaluating time
th
domain channel response
th
h tr (p)
The steps involved in proposed LMMSE channel
estimation algorithm is given below.
1: Input bits in the time domain is given to the OFDM
transceiver.
2: Modulation mode is defined & serial data is
converted into parallel data.
3: Pilot mode is defined i.e. Block type pilot.
4: Inverse Fast Fourier Transform (IFFT) takes place.
- (2)
x ( n )  IFFT X (K ) n=0,1…K-1
5: Guard intervals are introduced to eliminate Inter
Symbol Interference (ISI).
x  N  n ,n  Ng toNg 1
xf  n  
n  0,1,...,N 1
x  n ,
- (3)
Where Ng is the number of guard intervals and
Parallel data is converted into serial data.
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International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
6: Multipath flat fading channel is chosen where all
frequency components of the signal will experience
the same magnitude of fading.
yf  x f  n   h  n   w  n 
- (4)
7: Guard intervals are removed.
y n  yf  n n  0,1,...,N1
- (5)
8: Fast Fourier Transformation (FFT) takes place.
Y(K)  FFT y(n) k=0,1…K-1
– (6)
The output Y(K) can be expressed as
Y  K  X K  H  K  I K   W K 
- (7)
Where H(K) refers to channel, I(K) refers to
ICI and W(K) refers to AWGN (Additive White
Gaussian Noise).
9: Channel estimation is done using Linear Minimum
Mean Square Error (LMMSE) estimator.
Xe  K 
Y K
k  0,1,...,K 1
He  K
Fig. 2. SNR Vs BER
- (8)
Where X e(K) refers to the estimated output and
He(K) refers to the estimated channel.
V.RESULTS AND DISCUSSIONS
Fig.2.Shows simulation result of signal to noise
ratio verses bit error rate. From the simulation result, the
proposed method shows a gradual steep decrease in the
BER (Bit Error Rate) as the Signal to Noise Ratio (SNR)
increases.
The performance of proposed algorithm can be
shown in the simulation results by using MATLAB
7.9.The simulation parameters used are Total number of
user K=64, Total number of subcarriers N=256,
BER=1x10-1.5,
Bandwidth
B=50MHz,T=R=10,
Modulation & Demodulation Used =32QAM,Channel type
used = Multipath flat fading channel,OFDM symbol
length=40µs.
A. Simulation Results
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International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
Fig. 3. SNR Vs MSE
Fig.3.Shows simulation result of signal to noise
ratio verses Mean Squared Error. From the simulation
result, the proposed method shows a linear gradual steep
decrease in the BER (Bit Error Rate) as the Signal to Noise
Ratio (SNR) increases.
[7] X.G.Doukopoulos and G.V.Moustakides, “Blind
adaptive channel estimation in OFDM systems,” IEEE
Trans. Wireless Commun., vol. 5, no. 7, pp. 1716–
1725, July 2006.
[8] Christopher Knievel, Zhenyu Shi and Peter Adam
Hoeher,“2D Graph-Based Soft Channel Estimation
for MIMO-OFDM”,IEEE,2010.
VI.CONCLUSION
In this paper, an efficient LMMSE channel
estimation algorithm is proposed and its performance is
numerically confirmed for the MIMO-OFDM systems.
The results show that as compared with conventional
MMSE, using this scheme the computational burden is
reduced and suffer little attenuation of performances. The
Bit Error Rate (BER) and Mean Square Error (MSE)
shows improved performances when flat fading channel is
used. Therefore, it is rather attractive for practical
application in OFDM-based communication systems.
From the simulation results we can conclude that
the proposed LMMSE algorithm provides better
performance in terms of Mean Square Error (MSE) and Bit
Error Rate (BER) compared to conventional algorithms.
As future works may be extended to improve
channel capacity and less computational complexity.
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[2] Y.Li, L.J.Cimini and N.R.Sollenberger, “Robust
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[3] Navid daryasafar and Babak ehyaee , “Evaluation of
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Systems with Considering the Carrier Frequency
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[4] Y.Gong and K.Ben Letaief, “Low complexity
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[6] Jiun Siew, Robert Piechocki, Andrew Nix, and Simon
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