Abstract
This paper presents a novel genetic algorithm for jointly optimizing source and channel codes. The algorithm uses a channel-optimized vector quantizer for the source code, and a rate-punctured convolutional code for the channel code. The genetic algorithm enhances the robustness of the rate-distortion performance of the channel-optimized vector quantizer, and reduces the computational time for finding the best rate-punctured convolutional code. Numerical results show that the algorithm attains near optimal performance while having low computational complexity.
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© 2006 Springer-Verlag Berlin Heidelberg
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Ou, CM., Hwang, WJ., Yung, HC. (2006). Design of Robust Communication Systems Using Genetic Algorithms. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds) Genetic Programming. EuroGP 2006. Lecture Notes in Computer Science, vol 3905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11729976_24
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DOI: https://doi.org/10.1007/11729976_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33143-8
Online ISBN: 978-3-540-33144-5
eBook Packages: Computer ScienceComputer Science (R0)