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. © 2006 Springer-Verlag London Limited.
| Original language | English |
|---|---|
| Pages (from-to) | 1-9 |
| Journal | Neural Computing and Applications |
| Volume | 17 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2008 |
Research Keywords
- Self-organizing map
- Vector quantization
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