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Improved transmission of vector quantized data over noisy channels

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Abstract

The conventional channel-optimized vector quantization (COVQ) is very powerful in the protection of vector quantization (VQ) data over noisy channels. However, it suffers from the time consuming training process. A soft decoding self-organizing map (SOM) approach for VQ over noisy channels is presented. Compared with the COVQ approach, it does not require a long training time. For AWGN and fading channels, the distortion of the proposed approach is comparable to that of COVQ. Simulation confirmed that our proposed approach is a fast and practical method for VQ over noisy channels.

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Acknowledgment

This research was supported by a research grant from City University of Hong Kong (Project no. 7001819).

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Authors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China

    Chi-Sing Leung, John Sum & Herbert Chan

  2. Department of Information Management, Chung Shan Medical University, Taichung, Taiwan

    John Sum

  3. University of Science and Technology, Hong Kong, China

    Herbert Chan

Authors
  1. Chi-Sing Leung
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  2. John Sum
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  3. Herbert Chan
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Correspondence to Chi-Sing Leung.

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Leung, CS., Sum, J. & Chan, H. Improved transmission of vector quantized data over noisy channels. Neural Comput & Applic 17, 1–9 (2008). https://doi.org/10.1007/s00521-006-0073-7

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  • DOI: https://doi.org/10.1007/s00521-006-0073-7

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