Design of dfe based mimo communication system for mobile moving with high velocity

Design of DFE Based MIMO Communication System
for Mobile Moving with High Velocity
S.Bandopadhaya1
, L.P. Mishra2
, D.Swain3
, Mihir N.Mohanty4*
1,3
Dept of Electronics & Telecomunicationt,Silicon Institute of Technology,
Bhubaneswar,Odisha,India
1
shuva_bandopadhaya@ rediffmail.com
2,4
ITER,Siksha O Anusandhan University
Bhubaneswar,Odisha,India
2
lp_mishra@yahoo.co.in 4*
mihir.n.mohanty@gmail.com
Abstract – Reliability, high quality, and efficient data rate
communication for high speed mobile is the growing research in
recent years. In this paper time dispersive and frequency
dispersive effects on signal is analyzed. Then a decision feedback
equalizer is proposed whose weights are periodically updated
using LMS algorithm depending upon the statistical parameters
of fading channel. In order to combat the effect of fading a
MIMO wireless communication technique is used. Finally the
performance of the system under high time dispersive and
frequency dispersive channel is analyzed while the velocity of the
mobile is taken as high as 250km/hr.
Keywords – MIMO System, Fading, Diversity, DFE, LMS
Algorithm.
I. INTRODUCTION
Reliable high data communication over wireless
channel is highly demandable and challenging. Considerable
research has been done in last decades in the area of wireless
communication. The mobile broadband wireless access
(MBWA-IEEE802.20) standard describes a reliable
communication for data rate up to 1Mbps at a mobile speed of
250km/hr which is close to the standards of 4G[1]. The
bottleneck of reliable high data rate communication is that
when mobile is moving at very high velocity, the signal
strength and phase alters quite randomly due to high time
dispersive and frequency dispersive channel.
Diversity is one of the important aspect in wireless
communication. The most suitable method to overcome the
above mentioned problem is to use space diversity. Space
diversity exploits the random nature of radio propagation by
finding independent but highly uncorrelated signal path for
communication .The idea behind this is, if there are number of
independent paths then the probability of getting at least one
strong signal, is more [2].
 /
1 2,............, , 1 ( ) ... (1)
NE
r m NP e P
    
     
where σ1, σ2 ,……….., σm =instantaneous SNR of each
independent path..
σ=specific SNR threshold below which call will drop.
E =average SNR
N=Number of independent path.
The uncorrelated path can be obtained by using more
than one transmitting antenna and more than one receiving
antenna. If the antenna separations are more than half of the
wavelength then the received signal is highly uncorrelated[3].
In this work the time dispersive and frequency dispersive
effects of channel on signal is analyzed. Then the
improvement of bit error rate (BER) performance is observed
by using multiple inputs and multiple output (MIMO) antenna
system. Then a MIMO based communication system using
decision feedback equalizer is proposed whose weights are
periodically updated by using LMS algorithm, depending on
statistical parameters of channel like rms delay spread,
coherence time and coherence bandwidth.
II. STATISTICAL PARAMETERS OF FADING CHANNEL
Statistical parameters are most important for
accuracy and efficacy of the system. The mean excess delay
and rms delay spread are the statistical parameters which
grossly quantity the multi-path channel used. The mean excess
delay and rms delay spread parameters give a mathematical
insight to the time dispersive properties of multi-path channel.
The mean excess delay is the first moment of power delay
profile and is defined as
( )
... (2)
( )
k k
K
k
K
P
P
 





The rms delay spread is the square root of the second
central moment of the power delay profile is given by
S. Bandopadhaya et al. / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 1 (5) , 2010,319-323
319

2 2
( ) ... (3)   
III. MULTIPLE INPUT MULTIPLE OUTPUT (MIMO)
SYSTEM
A communication system characterized by use of Nt
transmitting antenna and Nr receiving antenna (Nt &Nr >1) is
generally called as multiple input multiple output (MIMO)
system. The equivalent low pass channel impulse response
between jth
transmitting antenna and ith
receiving antenna is
hji(τ:t) where ‘τ’ is the delay variable and ‘t’ is the time
variable [[5]-[9]].
The random time-varying channel is characterized by
Nt X Nr matrix
11 12 1
21 22 2
1 2
( ; ) ( ; ) ( ; )
( ; ) ( ; ) ( ; )
( ; ) ... (4)
( ; ) ( ; ) ( ; )
NT
NT
NR NR NRNT
h t h t h t
h t h t h t
H t
h t h t h t
  
  

  
 
 
 
 
 
 


   

If a signal Xj(t) is transmitted from jth
transmitting
antenna, then the signal received at ith
receiving antenna in
absence of noise is given by
1
1
( ) ( ; ) * ( )
( ; ) * ( ) ... (5)
NT
i ij j
j
NT
ij j
j
r t h t X t d
h t X d
  
  

 

 
 

 
 
where j=1,2,3…Nt
( ) ( ; )* ( ) ... (6)ir t H t X  
Considering additive noise at the ith
receiving antenna the
received signal ith
receiving antenna is
1
( ) ( ; ) * ( ) ( ) ... (7)
NT
i ij j i
j
r t h t S t d t   

 
    .
IV. DECISION FEEDBACK EQUALIZATION (DFE)
It is a non-linear type equalizer consisting of two
filters - feed forward and feedback filter. The input to the feed
forward filter is the received signal sequence while the
sequences of decisions on previously detected symbols are the
input to the feedback filter. In real time communication, the
length of training sequence plays a vital role on system
performance. Decision feedback equalizer is an excellent
equalizer structure which provides almost same result
comparison to linear equalizer with lesser training sequence.
The feedback filter used in DFE removes that part of ISI from
present estimated symbol caused by previously detected
symbols.
If the equalizer has (N1+1) forward weights and N2 feed back
weights then
 
2
2
1
0
1 2
1
ˆ , ,............. ...(8)
N
k j k j j k j k k k N
j N j
d Wr Wd d d d    
 
  
where ˆ
kd is the estimate of the kth
transmitted symbol.Wj are
the weights of the filter { k jd  } where j=1,2….N2 are the
previously detected symbols The weight updating of DFE is
done mostly on Mean-Square-Error (MSE) criterion. In this
criterion the weight of equalizer are adjusted to minimize the
means square value of error.
22
ˆ ... (9)k k kJ E E d d   
where dk is the information symbol transmitted in kth
signaling
interval and ˆ
kd is the estimated symbol [4][5].
V. SIMULATION RESULT
In this section the performance of MIMO wireless
communication system is analyzed using DFE (Decision
Feedback Equalizer) while the velocity of the receiver is about
250km/hour. Initially the dispersive property of fading
channel and the analysis of various aspects of MIMO channel
is followed. The SNR~BER performance of DFE based
systems is evaluated and obtained graphically, where the
equalizer weights are updated using LMS algorithm
periodically depending upon the statistical parameters of
fading channel. Various factors are summarized along with
their result as follows.
320

A. Dispersive effect of fading channel
Fig-2 shows the time dispersive and frequency
dispersive effect of channel on signal. For frequency
dispersive channel a Doppler shift of 200Hz (about
250Km/hour for 900Mhz frequency) and for time dispersive
channel three multi path delay components of path delays of
0,65ms and 130ms with average path gain of 0dB, -1dB and -
1.5dB respectively are introduced.
Fig-2: Dispersive effect of fading channel
B. Fading envelop of the channel and Eigen value analysis
Fig-3 and fig-4 shows fading envelop and the Eigen
value analysis of correlation matrix of MIMO channel
respectively with NR=2 and NT=1, 2, 3, 4.which shows that
with the increase of the number of antenna the fading nature
of the channel mitigates and the Eigen value spread also
decreases which improves the convergence rate of the
gradient-based LMS algorithm.
Fig-3 Fading envelop of MIMO channel
.
Fig-4 Eigen value analysis of MIMO channel
C. Noise analysis of MIMO systems
Fig-5 shows BER (bit error rate) performance of
MIMO systems in presence of noise with NR=2 and
NT=1,2,3,4.
321

Fig-5 Noise analysis of MIMO systems
D. Bit error rate (BER) performance of MIMO wireless
system with Decision feedback Equalizer (DFE)
Fig-7 presents a computer simulation of bit error
rate(BER) performance of MIMO wireless system with
Decision feedback equalizer (DFE) whose weights are
updated using statistical parameters of fading channel
Fig-6: DFE based MIMO Communication system
In this simulation the information bits are QAM
modulated and passed through a high time dispersive and
frequency dispersive channel using multiple input multiple
output (MIMO) scheme with NT=2,NR=1,2,3 by taking the
receiver velocity as approx. 250Km/hour at signal frequency
of 900MHz and three multipath components arriving at the
receiver of path delays of 0,250 µs and 300µs with average
path gains of 0dB,-2dB,-3dB respectively. The received signal
is passed through a decision feedback equalizer in order to
nullify the adverse effect of channel. The equalizer has 4 feed
forward weights and 2 feedback weights where the weights
are updated by LMS algorithm with step size of 0 .01.
Fig-7 SNR ~ BER plot for MIMO wireless channel with DFE
VI. CONCLUSION
It is observed from the simulation that when the
receiver velocity is about 250 km/hr, which is close to the
standards of IEEE 802.20, the bit error rate (BER)
performance of the system is quite satisfactory in presence of
a Decision feed back equalizer with proper weight
optimization. At the receiver bit error rate falls below 10-3
at
SNR value of 4dB and 10-4
at SNR value of 6dB and the
spectrum of MIMO channel response is flatter than individual
path response. Finally the paper concludes that as the number
of antenna increases the noise performance of the system
improves also it concludes that the improvement is marginal
comparing the performance of the systems with nt=2 and nr=2
hence it is advisable not to increase the number of receiving
antenna more than two that increases the system cost which
dominates the improvement of noise performance .The system
performance further will be improved by using efficient error
correction codes, OFDM and different spread spectrum
techniques which will allow the future researcher to go
forward.
REFERENCES
1- Szczodrak,M,Kim,J., Back ,Y “Szczodrak,M,Kim,J., Back ,Y
“4GM@4GW: Implementing 4G in the military mobile ad-hoc network
environment “. International Journal of computer science and Network
security. VOL.7 No.4 pp 70-79 April-2007.
2- Jakes, W.C.,” A comparison of specific space Diversity Technique for
Reduction of fast fading in u+f mobile radio system.” IEEE transaction
on Vehicular technology. Vt-20, No-4, PP-81-83 Nov 1971.
322

3- Jakes,W.C. “New Technique for mobile radio” Bell Laboratory Rec. PP-
326-330, Dec -1970
4- Rappaport, T.S., Huang , W., and Feuerstein, M.J., “Performance of
Decision feedback equalizers in simulated Urban and Indoor radio
channels”, IEICE Transaction on communications. VOL E76-B N)-2
Feb 1993.
5- Proakis, J.G, and Salehi, M, Communication systems engineering’.
Prentice Hall, 1994.
6- Rappaport, T.S. “Wireless Communications Principles and practice”.
Prentics Hall.
7- Bahari , S.M, Bendimerad F.T., “Transmitter design for LMS-MIMO-
MCCDMA system with pilot channel estimaties and zero forcing
equalizer “. International Journal of computer PP-258-263. Feb 2008
8- Sharma, S.K., Ahmad, S.N., “Performance of MIMO Space-TIME
coded Wireless communication system”. International conference on
computational Intelligence and Multimedia applications – 2007 PP-
373-378
9- Sanayei, S.and Nosratinia, A ., “Antenna selection in MIMO systems.”
IEEE communication Magazine. PP-68-73. Oct-2004.
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Design of dfe based mimo communication system for mobile moving with high velocity