MAP Detection and Robust Lossy Coding Over Soft-Decision Correlated Fading Channels

The joint source-channel coding (JSCC) problem for soft-decision-demodulated time-correlated fading channels is investigated without the use of channel coding and interleaving. For the purpose of system design, the recently introduced nonbinary noise discrete channel with queue-based (QB) noise (NBNDC-QB) is adopted. This analytically tractable Markovian model has been shown to represent effectively correlated fading channels that are hard to handle analytically. Optimal sequence maximum a posteriori (MAP) detection of a discrete Markov source sent over the NBNDC-QB is first studied. When the Markov source is binary and symmetric, a necessary and sufficient condition under which the MAP decoder is reduced to a simple instantaneous symbol-by-symbol decoder is established. Two robust lossy source coding schemes with a low encoding delay are next proposed for the NBNDC-QB. The first scheme consists of a scalar quantizer (SQ), a proper index assignment, and a sequence MAP decoder designed to harness the redundancy left in the indexes of the quantizer and the soft-decision output and the noise correlation of the channel. The second scheme is a classical noise-resilient vector quantizer known as the channel-optimized vector quantizer (COVQ). It is demonstrated that both systems can successfully exploit the memory and soft-decision information of the channel. Signal-to-distortion-ratio (SDR) gains of more than 1.7 dB are obtained over hard-decision demodulation by using only two bits for soft decision. Furthermore, gains as high as 4.4 dB can be achieved for a strongly correlated channel, in comparison with systems designed for the ideally interleaved (memoryless) channel. Finally, it is numerically observed that, for low coding rates, the NBNDC-QB model can accurately approximate discrete fading channels (DFCs) in terms of SDR performance.