Soft Decoding for Vector Quantization Over Noisy Channels with Memory

We provide a general treatment of optimal soft decoding for vector quantization over noisy channels with nite memory. The main result is a recursive implementation of optimal decoding. We also consider an approach to sub-optimal decoding, of lower complexity, being based on a generalization of the Viterbi algorithm. Finally we treat the problem of combined encoder{decoder design. Simulations compare the new decoders to a decision-based approach that uses Viterbi detection plus table look-up decoding. Optimal soft decoding signiicantly outperforms the benchmark decoder. The introduced sub-optimal decoder is able to perform close to the optimal and to outperform the benchmark scheme at a comparable complexity.