Concurrent genetic optimization for joint design of source and channel codes

Chien Min Ou; Wen Jyi Hwang; Wen Wei Hu; Tsung Yan Lo

doi:10.1016/j.neucom.2006.04.008

Concurrent genetic optimization for joint design of source and channel codes

Chien Min Ou, Wen Jyi Hwang^*, Wen Wei Hu, Tsung Yan Lo

^*Corresponding author for this work

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

A novel algorithm for jointly optimizing source and channel codes is presented in this paper. The algorithm uses the channel-optimized vector quantization (COVQ) for the source code, and rate-punctured convolutional coding (RCPC) for the channel code. The genetic algorithm (GA) is used for the concurrent design of both source and channel codes. The GA enhances the robustness of the rate-distortion performance of the COVQ to the selection of initial codewords. In addition, it reduces the computational time for realizing the unequal error protection scheme best matched to the COVQ. Numerical results show that the algorithm attains near optimal performance while having low computational complexity.

Original language	English
Pages (from-to)	130-138
Number of pages	9
Journal	Neurocomputing
Volume	70
Issue number	1-3
DOIs	https://doi.org/10.1016/j.neucom.2006.04.008
Publication status	Published - 2006 Dec

Keywords

Error correct coding
Genetic algorithm
Vector quantization

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2006.04.008

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title = "Concurrent genetic optimization for joint design of source and channel codes",

abstract = "A novel algorithm for jointly optimizing source and channel codes is presented in this paper. The algorithm uses the channel-optimized vector quantization (COVQ) for the source code, and rate-punctured convolutional coding (RCPC) for the channel code. The genetic algorithm (GA) is used for the concurrent design of both source and channel codes. The GA enhances the robustness of the rate-distortion performance of the COVQ to the selection of initial codewords. In addition, it reduces the computational time for realizing the unequal error protection scheme best matched to the COVQ. Numerical results show that the algorithm attains near optimal performance while having low computational complexity.",

keywords = "Error correct coding, Genetic algorithm, Vector quantization",

author = "Ou, {Chien Min} and Hwang, {Wen Jyi} and Hu, {Wen Wei} and Lo, {Tsung Yan}",

year = "2006",

month = dec,

doi = "10.1016/j.neucom.2006.04.008",

language = "English",

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TY - JOUR

T1 - Concurrent genetic optimization for joint design of source and channel codes

AU - Ou, Chien Min

AU - Hwang, Wen Jyi

AU - Hu, Wen Wei

AU - Lo, Tsung Yan

PY - 2006/12

Y1 - 2006/12

N2 - A novel algorithm for jointly optimizing source and channel codes is presented in this paper. The algorithm uses the channel-optimized vector quantization (COVQ) for the source code, and rate-punctured convolutional coding (RCPC) for the channel code. The genetic algorithm (GA) is used for the concurrent design of both source and channel codes. The GA enhances the robustness of the rate-distortion performance of the COVQ to the selection of initial codewords. In addition, it reduces the computational time for realizing the unequal error protection scheme best matched to the COVQ. Numerical results show that the algorithm attains near optimal performance while having low computational complexity.

AB - A novel algorithm for jointly optimizing source and channel codes is presented in this paper. The algorithm uses the channel-optimized vector quantization (COVQ) for the source code, and rate-punctured convolutional coding (RCPC) for the channel code. The genetic algorithm (GA) is used for the concurrent design of both source and channel codes. The GA enhances the robustness of the rate-distortion performance of the COVQ to the selection of initial codewords. In addition, it reduces the computational time for realizing the unequal error protection scheme best matched to the COVQ. Numerical results show that the algorithm attains near optimal performance while having low computational complexity.

KW - Error correct coding

KW - Genetic algorithm

KW - Vector quantization

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EP - 138

JO - Neurocomputing

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ER -

Concurrent genetic optimization for joint design of source and channel codes

Abstract

Keywords

ASJC Scopus subject areas

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