Abstract
We introduce new techniques for quantization over noisy channels with intersymbol interference. We focus on the decoding problem, and present a decoder structure that allows the decoding to be based on soft minimum mean square-error estimates of the transmitted bits. The new bit-estimate based decoder provides a structured lower-complexity approximation of optimal decoding for general codebooks, and for so-called linear mapping codebooks, it is shown that its implementation becomes particularly simple. We investigate decoding based on optimal bit-estimates, and on suboptimal estimates of lower computational complexity. We also consider encoder optimization and combined source-channel code design. Numerical simulations demonstrate that bit-estimate based decoding is able to outperform a two-stage decision-based approach implemented using Viterbi sequence detection plus table look-up source decoding. The simulations also show that decoding based on suboptimal bit-estimates performs well, at a considerably lowered complexity.
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