Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Self-organisation in Kohonen's SOM
Neural Networks
Conformal self-organization for continuity on a feature map
Neural Networks
Mobile Fading Channels
IEEE Transactions on Information Theory
Soft decoding for vector quantization over noisy channels with memory
IEEE Transactions on Information Theory
Transmission of vector quantized data over a noisy channel
IEEE Transactions on Neural Networks
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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.