Joint source-channel decoding in vector quantization over finite-state Markov channels and wireless channels

Abstract

We investigate joint source-channel decoding for vector quantization over the finite-state Markov channel. In particular, we consider the decoding problem in which the channel state information is not available to the decoder. Based on hidden Markov modeling of the channel output, two decoding strategies are proposed. The first one is a soft-decoder which estimates the source reconstruction vectors directly from the observed channel output samples. The second one is a hard-decoder based on joint MAP estimation of channel symbols and channel states. Application of these decoders to wireless channels with multipath fading is also investigated. Simulation results are provided for a flat fading wireless channel and a Gauss-Markov source.