MMSE decoding for vector quantization over channels with memory

Abstract

The paper presents memory-enhanced extensions of minimum mean-squared error (MMSE) decoding for vector quantization over noisy channels. We also develop a recursive algorithm for computing the transition probabilities of the Gilbert channel, and illustrate its performance in vector quantization of Gauss-Markov sources under noisy channel conditions. Simulation results indicate that the proposed algorithm enables the implementation of an MMSE decoder with increased robustness to channel errors.