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Soft-Decoding SOM for VQ Over Wireless Channels

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Abstract

A self-organizing map (SOM) approach for vector quantization (VQ) over wireless channels is presented. We introduce a soft decoding SOM-based robust VQ (RVQ) approach with performance comparable to that of the conventional channel optimized VQ (COVQ) approach. In particular, our SOM approach avoids the time-consuming index assignment process in traditional RVQs and does not require a reliable feedback channel for COVQ-like training. Simulation results show that our approach can offer potential performance gain over the conventional COVQ approach. For data sources with Gaussian distribution, the gain of our approach is demonstrated to be in the range of 1–4 dB. For image data, our approach gives a performance comparable to a sufficiently trained COVQ, and is superior with a similar number of training epoches. To further improve the performance, a SOM–based COVQ approach is also discussed.

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Authors and Affiliations

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

    Chi-Sing Leung & Herbert Chan

  2. Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Herbert Chan & Wai Ho Mow

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  1. Chi-Sing Leung
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  2. Herbert Chan
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  3. Wai Ho Mow
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Correspondence to Chi-Sing Leung.

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Leung, CS., Chan, H. & Mow, W.H. Soft-Decoding SOM for VQ Over Wireless Channels. Neural Process Lett 24, 179–192 (2006). https://doi.org/10.1007/s11063-006-9020-y

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