Genetic Algorithm Based Finite State Markov Channel Modeling

Rakesh Ranjan; Dipen Bepari; Debjani Mitra

doi:doi:10.11648/j.wcmc.20130104.13

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Genetic Algorithm Based Finite State Markov Channel Modeling

Rakesh Ranjan, Dipen Bepari, Debjani Mitra

Published in International Journal of Wireless Communications and Mobile Computing (Volume 1, Issue 4)

Received: 16 September 2013 Accepted: Published: 20 October 2013

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Abstract

Statistical properties of the error sequences produced by fading channels with memory have a strong influence over the performance of high layer protocols and error control codes. Finite State Markov Channel (FSMC) models can represent the temporal correlations of these sequences efficiently and accurately. This paper proposes a simple genetic algorithm (GA) based search for the optimum state transition matrix for a block diagonal Markov model. The burst error statistics of the GA based FSMC model with respect to Autocorrelation Function and error free interval distribution of the original error sequence are presented to validate the proposed method. The superiority of the GA approach over the semi-hidden Markov model (SHMM) based Fritchman model is exhibited in significant improvement of closeness of match and in the usage of shorter length of error sequences. Another Baum-Welch algorithm (BWA) based GA search method has been proposed and compared with the BWA and SHMM methods for the same error sequence. Again the superiority of GA approaches is recognized, especially for the smaller error lengths.

Published in	International Journal of Wireless Communications and Mobile Computing (Volume 1, Issue 4)
DOI	10.11648/j.wcmc.20130104.13
Page(s)	96-102
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Genetic Algorithm, Finite State Markov Channel, Semi-Hidden Markov Model, Baum-Welch Algorithm, Autocorrelation Functions, Error-Free Interval Distributions

References

[1]	H. S. Wang, and N. Moayeri, "Finite-State Markov Channel: A Useful Model for Radio Communication Channels," IEEE Transactions on Vehicular Technology, vol. 44, pp. 163–171, 1995.
[2]	E. N. Gilbert, "Capacity of a burst-noise channel," Bell System Technical Journal, vol. 39, pp. 1253–1265, 1960.
[3]	E. O. Elliott, "Estimates of error rates for codes on burst-noise channels," Bell System Technical Journal, vol. 42, pp. 1977–1997, 1963.
[4]	B. D. Fritchman, "A binary channel characterization using partitioned Markov chains," IEEE Transactions on Information Theory, vol. IT-13, pp. 221–227, 1967.
[5]	Shun-Zheng Yu, "Hidden semi-Markov models," Artificial Intelligence, vol. 174, pp. 215–243, 2010.
[6]	P. Sadeghi, R. Kennedy, P. Rapajic, and R. Shams, "Finite-state Markov modeling of fading channels - A survey of principles and applications," IEEE Signal Processing Magazine, vol. 25, pp. 57-80, 2008.
[7]	H. Bai, and M. Atiquzzaman, "Error modeling schemes for fading channels in wireless communications: A survey," IEEE Communications Surveys & Tutorials, vol. 5, pp. 2-9, 2003.
[8]	C. Pimentel, and I. F. Blake, "Enumeration of Markov chains and burst error statistics for finite-state channel models," IEEE Transactions on Vehicular Technology, vol. 48, pp. 415–428, 1999.
[9]	F. Babich, and G. Lombardi, "A Markov model for the mobile propagation channel," IEEE Transactions on Vehicular Technology, vol. 49, pp. 63-73, 2003.
[10]	C. C. Tan, and N. C. Beaulieu, "On first-order Markov modeling for the Rayleigh fading channel," IEEE Transactions on Communications, vol. 48, pp. 2032-2040, 2000.
[11]	N. Nefedov, "Generative Markov models for discrete channel modelling," The 8th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 1, pp. 7-11, 1997.
[12]	D. Whitley, "A genetic algorithm tutorial," Statistics and computing, vol. 4, pp. 65-85, 2003.
[13]	K. S. Tang, K. F. Man, S. Kwong, and Q. He, "Genetic algorithms and their applications," IEEE Signal Processing Magazine, vol. 13, pp. 22-37, 1996.
[14]	T. V. Mathew, Genetic algorithm. http://www.civil.iitb.ac.in/tvm/2701_dga/2701-ga-notes/gadoc.pdf.
[15]	S. Forrest, "Genetic algorithms: principles of natural selection applied to computation," Science, vol. 261, pp. 872-878, 1993.
[16]	A. Chipperfield, P. Fleming, H. Pohlheim, and C. Fonseca, Genetic Algorithm Toolbox: User's Guide, For Use with MATLAB, Version 1.2. http://www.pohlheim.com/Papers/tr_gatbx12/ChipperfieldFlemingPohlheimFonseca_tr_GATbx_v12.pdf.
[17]	S. O. C. Morales, Y. P. Maldonado, and F. T. Romero, "Improvement on Automatic Speech Recognition Using Micro-genetic Algorithm," in 11th Mexican International Conference on Artificial Intelligence, pp. 95-99, 2012.
[18]	R. Li, Jia-heng Zheng, and Chun-qin Pei, "Text Information Extraction Based on Genetic Algorithm and Hidden Markov Model," in First International Workshop on Education Technology and Computer Science, vol. 1, pp. 334-338, 2009.
[19]	J. Xiao, L. Zou, and C. Li, "Optimization of Hidden Markov Model by a Genetic Algorithm for Web Information Extraction," in International Conference on Intelligent Systems and Knowledge Engineering, 2007.
[20]	Z. Zhi-Jin, Z. Shi-Lian, X. Chun-Yun, and K. Xian-Zheng, "Discrete channel modeling based on genetic algorithm and simulated annealing for training hidden Markov model," Chinese Physics, vol. 16, pp. 1619-1623, 2007.
[21]	N. Surajudeen-Bakinde, Xu Zhu, J. Gao, and A. K. Nandi, "Genetic algorithm based equalization for direct sequence Ultra-Wideband communications systems," In Proceedings of the 2009 IEEE wireless communications and networking conference, pp. 145-149, 2009.
[22]	T. W. Rondeau, C. J. Rieser, T. M. Gallagher, and C. W. Bostian, "Online modeling of wireless channels with hidden Markov models and channel impulse responses for cognitive radios, In proceeding of 2004 IEEE International Microwave Symposium Digest, vol. 2, pp. 739-742, 2004.
[23]	W. H. Tranter, K. S. Shanmugan, T. S. Rappaport, and K. L. Kosbar, Principles of Communication Systems Simulation with Wireless Applications. Prentice Hall, NJ, Professional Technical Reference, 2004.
[24]	W. Turin, Performance Analysis and Modeling of Digital Transmission Systems. Kluwer Academic/Plenum Publishers, 2004.

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APA Style

Rakesh Ranjan, Dipen Bepari, Debjani Mitra. (2013). Genetic Algorithm Based Finite State Markov Channel Modeling. International Journal of Wireless Communications and Mobile Computing, 1(4), 96-102. https://doi.org/10.11648/j.wcmc.20130104.13

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ACS Style

Rakesh Ranjan; Dipen Bepari; Debjani Mitra. Genetic Algorithm Based Finite State Markov Channel Modeling. Int. J. Wirel. Commun. Mobile Comput. 2013, 1(4), 96-102. doi: 10.11648/j.wcmc.20130104.13

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AMA Style

Rakesh Ranjan, Dipen Bepari, Debjani Mitra. Genetic Algorithm Based Finite State Markov Channel Modeling. Int J Wirel Commun Mobile Comput. 2013;1(4):96-102. doi: 10.11648/j.wcmc.20130104.13

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@article{10.11648/j.wcmc.20130104.13,
  author = {Rakesh Ranjan and Dipen Bepari and Debjani Mitra},
  title = {Genetic Algorithm Based Finite State Markov Channel Modeling},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {1},
  number = {4},
  pages = {96-102},
  doi = {10.11648/j.wcmc.20130104.13},
  url = {https://doi.org/10.11648/j.wcmc.20130104.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20130104.13},
  abstract = {Statistical properties of the error sequences produced by fading channels with memory have a strong influence over the performance of high layer protocols and error control codes. Finite State Markov Channel (FSMC) models can represent the temporal correlations of these sequences efficiently and accurately. This paper proposes a simple genetic algorithm (GA) based search for the optimum state transition matrix for a block diagonal Markov model. The burst error statistics of the GA based FSMC model with respect to Autocorrelation Function and error free interval distribution of the original error sequence are presented to validate the proposed method. The superiority of the GA approach over the semi-hidden Markov model (SHMM) based Fritchman model is exhibited in significant improvement of closeness of match and in the usage of shorter length of error sequences. Another Baum-Welch algorithm (BWA) based GA search method has been proposed and compared with the BWA and SHMM methods for the same error sequence. Again the superiority of GA approaches is recognized, especially for the smaller error lengths.},
 year = {2013}
}

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TY  - JOUR
T1  - Genetic Algorithm Based Finite State Markov Channel Modeling
AU  - Rakesh Ranjan
AU  - Dipen Bepari
AU  - Debjani Mitra
Y1  - 2013/10/20
PY  - 2013
N1  - https://doi.org/10.11648/j.wcmc.20130104.13
DO  - 10.11648/j.wcmc.20130104.13
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 96
EP  - 102
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20130104.13
AB  - Statistical properties of the error sequences produced by fading channels with memory have a strong influence over the performance of high layer protocols and error control codes. Finite State Markov Channel (FSMC) models can represent the temporal correlations of these sequences efficiently and accurately. This paper proposes a simple genetic algorithm (GA) based search for the optimum state transition matrix for a block diagonal Markov model. The burst error statistics of the GA based FSMC model with respect to Autocorrelation Function and error free interval distribution of the original error sequence are presented to validate the proposed method. The superiority of the GA approach over the semi-hidden Markov model (SHMM) based Fritchman model is exhibited in significant improvement of closeness of match and in the usage of shorter length of error sequences. Another Baum-Welch algorithm (BWA) based GA search method has been proposed and compared with the BWA and SHMM methods for the same error sequence. Again the superiority of GA approaches is recognized, especially for the smaller error lengths.
VL  - 1
IS  - 4
ER  -

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Author Information

Rakesh Ranjan

Department of Electronics and Communication Engineering, National Institute of Technology (NIT), Patna, 800005, India
Dipen Bepari

Department of Electronics Engineering, Indian School of Mines (ISM), Dhanbad, 826004, India
Debjani Mitra

Department of Electronics Engineering, Indian School of Mines (ISM), Dhanbad, 826004, India

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Plain Text BibTeX RIS

APA Style

Rakesh Ranjan, Dipen Bepari, Debjani Mitra. (2013). Genetic Algorithm Based Finite State Markov Channel Modeling. International Journal of Wireless Communications and Mobile Computing, 1(4), 96-102. https://doi.org/10.11648/j.wcmc.20130104.13

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ACS Style

Rakesh Ranjan; Dipen Bepari; Debjani Mitra. Genetic Algorithm Based Finite State Markov Channel Modeling. Int. J. Wirel. Commun. Mobile Comput. 2013, 1(4), 96-102. doi: 10.11648/j.wcmc.20130104.13

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AMA Style

Rakesh Ranjan, Dipen Bepari, Debjani Mitra. Genetic Algorithm Based Finite State Markov Channel Modeling. Int J Wirel Commun Mobile Comput. 2013;1(4):96-102. doi: 10.11648/j.wcmc.20130104.13

Copy | Download

@article{10.11648/j.wcmc.20130104.13,
  author = {Rakesh Ranjan and Dipen Bepari and Debjani Mitra},
  title = {Genetic Algorithm Based Finite State Markov Channel Modeling},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {1},
  number = {4},
  pages = {96-102},
  doi = {10.11648/j.wcmc.20130104.13},
  url = {https://doi.org/10.11648/j.wcmc.20130104.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20130104.13},
  abstract = {Statistical properties of the error sequences produced by fading channels with memory have a strong influence over the performance of high layer protocols and error control codes. Finite State Markov Channel (FSMC) models can represent the temporal correlations of these sequences efficiently and accurately. This paper proposes a simple genetic algorithm (GA) based search for the optimum state transition matrix for a block diagonal Markov model. The burst error statistics of the GA based FSMC model with respect to Autocorrelation Function and error free interval distribution of the original error sequence are presented to validate the proposed method. The superiority of the GA approach over the semi-hidden Markov model (SHMM) based Fritchman model is exhibited in significant improvement of closeness of match and in the usage of shorter length of error sequences. Another Baum-Welch algorithm (BWA) based GA search method has been proposed and compared with the BWA and SHMM methods for the same error sequence. Again the superiority of GA approaches is recognized, especially for the smaller error lengths.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Genetic Algorithm Based Finite State Markov Channel Modeling
AU  - Rakesh Ranjan
AU  - Dipen Bepari
AU  - Debjani Mitra
Y1  - 2013/10/20
PY  - 2013
N1  - https://doi.org/10.11648/j.wcmc.20130104.13
DO  - 10.11648/j.wcmc.20130104.13
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 96
EP  - 102
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20130104.13
AB  - Statistical properties of the error sequences produced by fading channels with memory have a strong influence over the performance of high layer protocols and error control codes. Finite State Markov Channel (FSMC) models can represent the temporal correlations of these sequences efficiently and accurately. This paper proposes a simple genetic algorithm (GA) based search for the optimum state transition matrix for a block diagonal Markov model. The burst error statistics of the GA based FSMC model with respect to Autocorrelation Function and error free interval distribution of the original error sequence are presented to validate the proposed method. The superiority of the GA approach over the semi-hidden Markov model (SHMM) based Fritchman model is exhibited in significant improvement of closeness of match and in the usage of shorter length of error sequences. Another Baum-Welch algorithm (BWA) based GA search method has been proposed and compared with the BWA and SHMM methods for the same error sequence. Again the superiority of GA approaches is recognized, especially for the smaller error lengths.
VL  - 1
IS  - 4
ER  -

Copy | Download