Soft-decoding SOM for VQ over wireless channels
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Related Research Unit(s)
|Journal / Publication||Neural Processing Letters|
|Publication status||Published - Oct 2006|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-33750009884&origin=recordpage|
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. © Springer Science+Business Media, LLC 2006.
- Self-organizing map, Soft-decoding, Vector quantization, Wireless channels