Soft-decoding based vector quantization for hidden-Markov channels

Abstract

Summary form only given. Channel optimized vector quantization (COVQ) has received considerable attention as an approach to joint source-channel coding. COVQ for hidden Markov channels was studied, with application in wireless communication over fading channels. The optimal VQ decoding was considered for finite state channels when the channel state is not explicitly observed at the receiver. A hidden Markov model (HMM) with a continuous observation space is constructed for the channel by assuming both channel input and channel state are first-order Markov process. A recursive-decoding algorithm is derived for computing the minimum mean square error (MMSE) optimal estimate for the source vector, based on the observed channel output sequence. In simulation experiments, the performance of a communications system based on the proposed quantizer is investigated for a Gauss-Markov signal source and a frequency non-selective Rayleigh fading channel. The results indicate that for fading channels, proposed soft-decoding based COVQ can results in a substantial improvement of performance over detection based COVQ.