Concurrent genetic optimization for joint design of source and channel codes

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

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)130-138
Number of pages9
JournalNeurocomputing
Volume70
Issue number1-3
DOIs
Publication statusPublished - 2006 Dec 1

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Vector quantization
Genetic algorithms
Computational complexity

Keywords

  • Error correct coding
  • Genetic algorithm
  • Vector quantization

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

Concurrent genetic optimization for joint design of source and channel codes. / Ou, Chien Min; Hwang, Wen-Jyi; Hu, Wen Wei; Lo, Tsung Yan.

In: Neurocomputing, Vol. 70, No. 1-3, 01.12.2006, p. 130-138.

Research output: Contribution to journalArticle

Ou, Chien Min ; Hwang, Wen-Jyi ; Hu, Wen Wei ; Lo, Tsung Yan. / Concurrent genetic optimization for joint design of source and channel codes. In: Neurocomputing. 2006 ; Vol. 70, No. 1-3. pp. 130-138.
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