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@ IJTSRD | Available Online @ www.ijtsrd.com
ISSN No: 2456
International
Research
Performance Analysis
Mean Square Error Equalizer Technique
Tamashri Sonartiya
M.Tech
Department of Electronics and Communication Engineering
Lakshmi Narain Coll
ABSTRACT
The V-BLAST MIMO-system some of linear
detection techniques can be used for interference
cancellation (IC). In this paper, using Minimum Mean
Square Error- interference cancellation for the same.
In this paper, we analysis of BER performance of
Vertical Bell Labs Layered Space Time Architecture
(V-BLAST) spatial Multiplexing Technique with
equalisation techniques like Minimum Mean Square
Error (MMSE) by BPSK modulation techniques in
Rayleigh flat fading channel.
Keywords: V-Blast, BPSK, MMSE, Equalizer,
interference cancellation, Rayleigh Channel.
I. INTRODUCTION
The BLAST architecture, introduced by Foschini [1
2] in 1996, is a low complexity transceiver
architecture to communicate over the MIMO wireless
channel. Although suboptimal, it is able to attain a
significant fraction of the theoretical MIMO capacity
over the rich-scattering wireless channel. BLAST
implies independent transmission of streams at the
transmitter side and successive interference
cancellation (SIC) at the receiver. In 1998, V
(Vertical BLAST) was introduced in [2] as a low
complexity wireless communication architecture. In
the V-BLAST, the layering is horizontal, meaning
that all the symbols of a certain stream are transmitted
through the same antenna (one stream per antenna). It
was shown in [3] that this architecture is able to
achieve very high spectral efficiencies e.g. spectral
efficiencies in the order of 20 – 40 bits/sec/Hz in an
indoor propagation environment at realistic SNR’s
and error rates.
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 5 | Jul-Aug 2018
ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume
International Journal of Trend in Scientific
Research and Development (IJTSRD)
International Open Access Journal
Performance Analysis of V-Blast MIMO System Using Minimum
Mean Square Error Equalizer Technique with BPSK
Tamashri Sonartiya1
, Deepak Pancholi2
M.Tech Student1
, Assistant Professor2
of Electronics and Communication Engineering
Lakshmi Narain College of Technology, Indore, Madhya Pradesh, India
system some of linear
detection techniques can be used for interference
cancellation (IC). In this paper, using Minimum Mean
interference cancellation for the same.
is paper, we analysis of BER performance of
Vertical Bell Labs Layered Space Time Architecture
BLAST) spatial Multiplexing Technique with
equalisation techniques like Minimum Mean Square
Error (MMSE) by BPSK modulation techniques in
Blast, BPSK, MMSE, Equalizer,
interference cancellation, Rayleigh Channel.
The BLAST architecture, introduced by Foschini [1-
2] in 1996, is a low complexity transceiver
architecture to communicate over the MIMO wireless
channel. Although suboptimal, it is able to attain a
significant fraction of the theoretical MIMO capacity
scattering wireless channel. BLAST
implies independent transmission of streams at the
transmitter side and successive interference
cancellation (SIC) at the receiver. In 1998, V-BLAST
(Vertical BLAST) was introduced in [2] as a low
eless communication architecture. In
BLAST, the layering is horizontal, meaning
that all the symbols of a certain stream are transmitted
through the same antenna (one stream per antenna). It
was shown in [3] that this architecture is able to
very high spectral efficiencies e.g. spectral
40 bits/sec/Hz in an
indoor propagation environment at realistic SNR’s
Fig. 1: V-Blast Algorithms
II. EQUALIZER TECHNIQUE
Equalization is the technique which is used to mitigate
the effect of the ISI (inter symbol interference) by
minimizing the error probability occur in the
communication system without ISI suppression
method. Since, suppression of ISI causes the noise
power to enhance therefore it is essential to create
optimum balance between enhancement of noise
power and suppression of ISI [6], one of the practical
problems in digital communications is inter
interference (ISI), which causes a given transmitted
symbol to be distorted by other transmitted symbols.
One of the most commonly used techniques to counter
the channel distortion (ISI) is linear channel
equalization. Conventional equalization techniques
employ a pre-assigned time slot (periodic for the time
varying situation). In the receiver the equalizer
coefficients are then changed (e.g. LMS, MMSE,
etc.).
A. MMSE Equalizer
This type of equalizer applies the squared error for
performance measurement. The receiver filter is
designed and develops to satisfy
square error criterion. Main objective of this
Aug 2018 Page: 310
6470 | www.ijtsrd.com | Volume - 2 | Issue – 5
Scientific
(IJTSRD)
International Open Access Journal
Blast MIMO System Using Minimum
ith BPSK
India
Blast Algorithms
TECHNIQUE
Equalization is the technique which is used to mitigate
the effect of the ISI (inter symbol interference) by
minimizing the error probability occur in the
communication system without ISI suppression
method. Since, suppression of ISI causes the noise
to enhance therefore it is essential to create
optimum balance between enhancement of noise
power and suppression of ISI [6], one of the practical
problems in digital communications is inter-symbol
interference (ISI), which causes a given transmitted
ol to be distorted by other transmitted symbols.
One of the most commonly used techniques to counter
the channel distortion (ISI) is linear channel
equalization. Conventional equalization techniques
assigned time slot (periodic for the time-
rying situation). In the receiver the equalizer
coefficients are then changed (e.g. LMS, MMSE,
This type of equalizer applies the squared error for
performance measurement. The receiver filter is
designed and develops to satisfy the minimum mean
square error criterion. Main objective of this
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 5 | Jul-Aug 2018 Page: 311
technique is to minimize the error produced between
target signal and output obtained by filter [6, 10].
III. COMMUNICATION CHANNEL
Due to the multipath characteristic of the wireless
propagation channel, multiple copies of the
transmitted signal arrive at the receiver at different
moments of time. This combination of signals with a
phase difference at the receiver causes abrupt
variations in the received signal power or in the
received SNR, which is known as fading. The NLOS
(indoor, city) Rayleigh fading occurs when there is no
multipath LOS between transmitter and receiver and
have only indirect path which is called NLOS to
receive the resultant waves. The Rayleigh Fading is
one kind of model which propagates the environment
of radio signal. Rayleigh fading works as a reasonable
model when many objects in environment which
scatter radio signal before arriving of receiver. When
there is no propagation dominant during line of sight
between transmitter and receiver on that time
Rayleigh Fading is most applicable.
Fig. 2: NLOS Prorogation
IV. MIMO-System
The two representations of space diversity techniques
are transmit diversity where multiple transmit
antennas are used (also known as multiple-input
single-output or MISO systems) and receive (Rx)
diversity where multiple receive antennas are used
(also 5 known as single-input multiple-output or
SIMO systems). In both cases it is required for
antennas to be placed sufficiently far apart so that the
channel gains between different antenna pairs fade
independently. The achievable capacity and
performance depend on the channel conditions and on
the structure of the transmit signal. In order to achieve
the goal the design MIMO system architecture
influences the complexity of the transmitter and,
particularly the receiver [4].
Fig. 3: MIMO Transceiver Structure [5]
V. SIMULATION PARAMETERS
In this work, we consider MMSE Equalizers
techniques with MIMO system and following
simulation parameters are considered for simulation
of results as shown in table 1.
Table: 1 Simulation Parameters
PARAMETERS VALUES
Technology V-BLAST
Data Bits 106
Number of Transmitter 2
Number of Receiver 2
Algorithm ZF and MMSE
Modulation Techniques BPSK
Channel Rayleigh, AWGN
Simulation toll Matlab R2013a
Calculation Parameters BER V/S SNR
X-axis SNR in dB
Y-axis BER
Technology Wireless
Communication
VI. SIMULATION RESULTS FOR MMSE
EQUALIZERS
In this section results are shown in terms of Bit Error
Rate (BER) with respect to variation in SNR for 2X2
MIMO-V-BLAST system using BPSK modulation
techniques and MMSE, MMSE-SIC and MMSE-SIC-
Sort equalisation algorithms under Rayleigh channels.
Fig. 3: Comparison Results for BPSK modulation
with 2×2 MIMO with different MMSE
0 5 10 15 20 25 30
10
-5
10
-4
10
-3
10
-2
10
-1
Average Eb/No,dB
BitErrorRate
BER for BPSK modulation with 2x2 MIMO and Comparision equalizer (Rayleigh channel)
sim (nTx=2, nRx=2, MMSE-SIC-Sort)
sim (nTx=2, nRx=2, MMSE-SIC)
sim (nTx=2, nRx=2, MMSE)
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 5 | Jul-Aug 2018 Page: 312
The BER values have been computed as a function of
SNR for BPSK modulation and different
combinations of MIMO systems using MMSE,
MMSE-SIC and Modified MMSE-SIC-Sort, and ML
equalizers. The simulations studies have been carried
out using MATLAB software. In the above result
observed that MMSE-SIC-Sort has reduced error at
all the point of SNR range 0 to 27dB.
VII. CONCLUSION
The MIMO systems are gaining much more attention
and efforts in wireless communication research due to
their potential to increase considerable capacity in
mobile cellular communication. The BER values have
been computed as a function of SNR for BPSK
modulation and different combinations of MIMO
systems using MMSE, MMSE-SIC and Modified
MMSE-SIC-Sort, and ML equalizers. The simulations
studies have been carried out using MATLAB
software. In the above result observed that MMSE-
SIC-Sort has reduced error at all the point of SNR
range 0 to 27dB.
REFERENCES
1. G. J. Foschini, “Layered Space-Time Architecture
for Wireless Communication in a Fading
Environment When Using Multiple Antennas,”
Bell Lab. Tech. J., v. 1, N. 2, pp. 41- 59, 1996.
2. G. J. Foschini, P. W. Wolnainsky, G. D. Golden,
and R. A. Valenzuela, “Simplified Processing for
High Spectral Efficiency Wireless
Communication Employing Multi-Element
Arrays,” IEEE J. Select. Areas Commun., vol. 17,
no. 11, pp. 1841–1852, Nov. 1999.
3. P. W. Wolnainsky, G. J. Foschini, G. D. Golden,
and R. A. Valenzuela, “V-BLAST: An
Architecture for Achieving Very High Data Rates
Over the Rich-Scattering Wireless Channel,” Int.
Symposium on Signals, Systems and Electronics
(ISSSE), Pisa, Italy, 1998.
4. Priya Sharma and Vijay Prakash Singh,
Performance Evaluation of V-Blast MIMO
System in Fading Diversity Using Matched Filter,
International Journal of Emerging Technology and
Advanced Engineering, Vol. 3(5), 2013.
5. Sabuj Sarkar “An Advanced Detection Technique
in MIMO--PSK Wireless Communication
Systems Using MMSE-SIC Detection over a
Rayleigh Fading Channel” Springer 2016.
6. Chandani Dewangan and Pankaj M Gulhane
“Performance Evaluation of Different
Equalization Techniques for 2x2 MIMO Wireless
Communication Systems” International Journal of
Advanced Research in Education & Technology
(IJARET), Vol. 3, Issue 3, 2016.
7. D. W. Tufts, “Nyquist’s problem-The joint
optimization of transmitter and receiver in pulse
amplitude modulation,” Proc. IEEE, vol. 53, pp.
248-260, Mar. 1965.
8. Anuj Kanchan, Shashank Dwivedi, “Comparison
of BER Performance in OFDM Using Different
Equalization Techniques”, International Journal of
Engineering and Advanced Technology (IJEAT)
ISSN: 2249 – 8958, Volume-1, Issue-6, August
2012, pp. 139-143.
9. Priya Sharma and Vijay Prakash Singh,
“Performance Evaluation of V-Blast MIMO
System in Fading Diversity Using Matched
Filter”, International Journal of Emerging
Technology and Advanced Engineering, Vol. 3(5).
2013.
10. Nirmalendu Bikas Sinha, S. Chakraborty, P. K.
Sutradhar, R. Bera, and M. Mitra, “Optimization
of MIMO Detectors: Unleashing the Multiplexing
Gain” Journal of Telecommunication, Vol.1, 335-
342, Feb. 2011.
11. Samarendra Nath Sur, “Performance Analysis of
V-BLAST MIMO System in Rician Channel
Environment”, Journal of Theoretical and Applied
Information Technology, Vol. 2, 597-601, 2011.

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Performance Analysis of V-Blast MIMO System Using Minimum Mean Square Error Equalizer Technique with BPSK

  • 1. @ IJTSRD | Available Online @ www.ijtsrd.com ISSN No: 2456 International Research Performance Analysis Mean Square Error Equalizer Technique Tamashri Sonartiya M.Tech Department of Electronics and Communication Engineering Lakshmi Narain Coll ABSTRACT The V-BLAST MIMO-system some of linear detection techniques can be used for interference cancellation (IC). In this paper, using Minimum Mean Square Error- interference cancellation for the same. In this paper, we analysis of BER performance of Vertical Bell Labs Layered Space Time Architecture (V-BLAST) spatial Multiplexing Technique with equalisation techniques like Minimum Mean Square Error (MMSE) by BPSK modulation techniques in Rayleigh flat fading channel. Keywords: V-Blast, BPSK, MMSE, Equalizer, interference cancellation, Rayleigh Channel. I. INTRODUCTION The BLAST architecture, introduced by Foschini [1 2] in 1996, is a low complexity transceiver architecture to communicate over the MIMO wireless channel. Although suboptimal, it is able to attain a significant fraction of the theoretical MIMO capacity over the rich-scattering wireless channel. BLAST implies independent transmission of streams at the transmitter side and successive interference cancellation (SIC) at the receiver. In 1998, V (Vertical BLAST) was introduced in [2] as a low complexity wireless communication architecture. In the V-BLAST, the layering is horizontal, meaning that all the symbols of a certain stream are transmitted through the same antenna (one stream per antenna). It was shown in [3] that this architecture is able to achieve very high spectral efficiencies e.g. spectral efficiencies in the order of 20 – 40 bits/sec/Hz in an indoor propagation environment at realistic SNR’s and error rates. @ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 5 | Jul-Aug 2018 ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume International Journal of Trend in Scientific Research and Development (IJTSRD) International Open Access Journal Performance Analysis of V-Blast MIMO System Using Minimum Mean Square Error Equalizer Technique with BPSK Tamashri Sonartiya1 , Deepak Pancholi2 M.Tech Student1 , Assistant Professor2 of Electronics and Communication Engineering Lakshmi Narain College of Technology, Indore, Madhya Pradesh, India system some of linear detection techniques can be used for interference cancellation (IC). In this paper, using Minimum Mean interference cancellation for the same. is paper, we analysis of BER performance of Vertical Bell Labs Layered Space Time Architecture BLAST) spatial Multiplexing Technique with equalisation techniques like Minimum Mean Square Error (MMSE) by BPSK modulation techniques in Blast, BPSK, MMSE, Equalizer, interference cancellation, Rayleigh Channel. The BLAST architecture, introduced by Foschini [1- 2] in 1996, is a low complexity transceiver architecture to communicate over the MIMO wireless channel. Although suboptimal, it is able to attain a significant fraction of the theoretical MIMO capacity scattering wireless channel. BLAST implies independent transmission of streams at the transmitter side and successive interference cancellation (SIC) at the receiver. In 1998, V-BLAST (Vertical BLAST) was introduced in [2] as a low eless communication architecture. In BLAST, the layering is horizontal, meaning that all the symbols of a certain stream are transmitted through the same antenna (one stream per antenna). It was shown in [3] that this architecture is able to very high spectral efficiencies e.g. spectral 40 bits/sec/Hz in an indoor propagation environment at realistic SNR’s Fig. 1: V-Blast Algorithms II. EQUALIZER TECHNIQUE Equalization is the technique which is used to mitigate the effect of the ISI (inter symbol interference) by minimizing the error probability occur in the communication system without ISI suppression method. Since, suppression of ISI causes the noise power to enhance therefore it is essential to create optimum balance between enhancement of noise power and suppression of ISI [6], one of the practical problems in digital communications is inter interference (ISI), which causes a given transmitted symbol to be distorted by other transmitted symbols. One of the most commonly used techniques to counter the channel distortion (ISI) is linear channel equalization. Conventional equalization techniques employ a pre-assigned time slot (periodic for the time varying situation). In the receiver the equalizer coefficients are then changed (e.g. LMS, MMSE, etc.). A. MMSE Equalizer This type of equalizer applies the squared error for performance measurement. The receiver filter is designed and develops to satisfy square error criterion. Main objective of this Aug 2018 Page: 310 6470 | www.ijtsrd.com | Volume - 2 | Issue – 5 Scientific (IJTSRD) International Open Access Journal Blast MIMO System Using Minimum ith BPSK India Blast Algorithms TECHNIQUE Equalization is the technique which is used to mitigate the effect of the ISI (inter symbol interference) by minimizing the error probability occur in the communication system without ISI suppression method. Since, suppression of ISI causes the noise to enhance therefore it is essential to create optimum balance between enhancement of noise power and suppression of ISI [6], one of the practical problems in digital communications is inter-symbol interference (ISI), which causes a given transmitted ol to be distorted by other transmitted symbols. One of the most commonly used techniques to counter the channel distortion (ISI) is linear channel equalization. Conventional equalization techniques assigned time slot (periodic for the time- rying situation). In the receiver the equalizer coefficients are then changed (e.g. LMS, MMSE, This type of equalizer applies the squared error for performance measurement. The receiver filter is designed and develops to satisfy the minimum mean square error criterion. Main objective of this
  • 2. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 @ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 5 | Jul-Aug 2018 Page: 311 technique is to minimize the error produced between target signal and output obtained by filter [6, 10]. III. COMMUNICATION CHANNEL Due to the multipath characteristic of the wireless propagation channel, multiple copies of the transmitted signal arrive at the receiver at different moments of time. This combination of signals with a phase difference at the receiver causes abrupt variations in the received signal power or in the received SNR, which is known as fading. The NLOS (indoor, city) Rayleigh fading occurs when there is no multipath LOS between transmitter and receiver and have only indirect path which is called NLOS to receive the resultant waves. The Rayleigh Fading is one kind of model which propagates the environment of radio signal. Rayleigh fading works as a reasonable model when many objects in environment which scatter radio signal before arriving of receiver. When there is no propagation dominant during line of sight between transmitter and receiver on that time Rayleigh Fading is most applicable. Fig. 2: NLOS Prorogation IV. MIMO-System The two representations of space diversity techniques are transmit diversity where multiple transmit antennas are used (also known as multiple-input single-output or MISO systems) and receive (Rx) diversity where multiple receive antennas are used (also 5 known as single-input multiple-output or SIMO systems). In both cases it is required for antennas to be placed sufficiently far apart so that the channel gains between different antenna pairs fade independently. The achievable capacity and performance depend on the channel conditions and on the structure of the transmit signal. In order to achieve the goal the design MIMO system architecture influences the complexity of the transmitter and, particularly the receiver [4]. Fig. 3: MIMO Transceiver Structure [5] V. SIMULATION PARAMETERS In this work, we consider MMSE Equalizers techniques with MIMO system and following simulation parameters are considered for simulation of results as shown in table 1. Table: 1 Simulation Parameters PARAMETERS VALUES Technology V-BLAST Data Bits 106 Number of Transmitter 2 Number of Receiver 2 Algorithm ZF and MMSE Modulation Techniques BPSK Channel Rayleigh, AWGN Simulation toll Matlab R2013a Calculation Parameters BER V/S SNR X-axis SNR in dB Y-axis BER Technology Wireless Communication VI. SIMULATION RESULTS FOR MMSE EQUALIZERS In this section results are shown in terms of Bit Error Rate (BER) with respect to variation in SNR for 2X2 MIMO-V-BLAST system using BPSK modulation techniques and MMSE, MMSE-SIC and MMSE-SIC- Sort equalisation algorithms under Rayleigh channels. Fig. 3: Comparison Results for BPSK modulation with 2×2 MIMO with different MMSE 0 5 10 15 20 25 30 10 -5 10 -4 10 -3 10 -2 10 -1 Average Eb/No,dB BitErrorRate BER for BPSK modulation with 2x2 MIMO and Comparision equalizer (Rayleigh channel) sim (nTx=2, nRx=2, MMSE-SIC-Sort) sim (nTx=2, nRx=2, MMSE-SIC) sim (nTx=2, nRx=2, MMSE)
  • 3. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 @ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 5 | Jul-Aug 2018 Page: 312 The BER values have been computed as a function of SNR for BPSK modulation and different combinations of MIMO systems using MMSE, MMSE-SIC and Modified MMSE-SIC-Sort, and ML equalizers. The simulations studies have been carried out using MATLAB software. In the above result observed that MMSE-SIC-Sort has reduced error at all the point of SNR range 0 to 27dB. VII. CONCLUSION The MIMO systems are gaining much more attention and efforts in wireless communication research due to their potential to increase considerable capacity in mobile cellular communication. The BER values have been computed as a function of SNR for BPSK modulation and different combinations of MIMO systems using MMSE, MMSE-SIC and Modified MMSE-SIC-Sort, and ML equalizers. The simulations studies have been carried out using MATLAB software. In the above result observed that MMSE- SIC-Sort has reduced error at all the point of SNR range 0 to 27dB. REFERENCES 1. G. J. Foschini, “Layered Space-Time Architecture for Wireless Communication in a Fading Environment When Using Multiple Antennas,” Bell Lab. Tech. J., v. 1, N. 2, pp. 41- 59, 1996. 2. G. J. Foschini, P. W. Wolnainsky, G. D. Golden, and R. A. Valenzuela, “Simplified Processing for High Spectral Efficiency Wireless Communication Employing Multi-Element Arrays,” IEEE J. Select. Areas Commun., vol. 17, no. 11, pp. 1841–1852, Nov. 1999. 3. P. W. Wolnainsky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela, “V-BLAST: An Architecture for Achieving Very High Data Rates Over the Rich-Scattering Wireless Channel,” Int. Symposium on Signals, Systems and Electronics (ISSSE), Pisa, Italy, 1998. 4. Priya Sharma and Vijay Prakash Singh, Performance Evaluation of V-Blast MIMO System in Fading Diversity Using Matched Filter, International Journal of Emerging Technology and Advanced Engineering, Vol. 3(5), 2013. 5. Sabuj Sarkar “An Advanced Detection Technique in MIMO--PSK Wireless Communication Systems Using MMSE-SIC Detection over a Rayleigh Fading Channel” Springer 2016. 6. Chandani Dewangan and Pankaj M Gulhane “Performance Evaluation of Different Equalization Techniques for 2x2 MIMO Wireless Communication Systems” International Journal of Advanced Research in Education & Technology (IJARET), Vol. 3, Issue 3, 2016. 7. D. W. Tufts, “Nyquist’s problem-The joint optimization of transmitter and receiver in pulse amplitude modulation,” Proc. IEEE, vol. 53, pp. 248-260, Mar. 1965. 8. Anuj Kanchan, Shashank Dwivedi, “Comparison of BER Performance in OFDM Using Different Equalization Techniques”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-6, August 2012, pp. 139-143. 9. Priya Sharma and Vijay Prakash Singh, “Performance Evaluation of V-Blast MIMO System in Fading Diversity Using Matched Filter”, International Journal of Emerging Technology and Advanced Engineering, Vol. 3(5). 2013. 10. Nirmalendu Bikas Sinha, S. Chakraborty, P. K. Sutradhar, R. Bera, and M. Mitra, “Optimization of MIMO Detectors: Unleashing the Multiplexing Gain” Journal of Telecommunication, Vol.1, 335- 342, Feb. 2011. 11. Samarendra Nath Sur, “Performance Analysis of V-BLAST MIMO System in Rician Channel Environment”, Journal of Theoretical and Applied Information Technology, Vol. 2, 597-601, 2011.