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 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), 2013. 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

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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