COVQ for MAP hard-decision demodulated channels

Authors:
Hamidreza Ebrahimzadeh Saffar;Fady Alajaji;Tamás Linder
Affiliations:
Department of Mathematics and Statistics, Queen’s University, Kingston, ON, Canada;Department of Mathematics and Statistics, Queen’s University, Kingston, ON, Canada;Department of Mathematics and Statistics, Queen’s University, Kingston, ON, Canada
Venue:
IEEE Communications Letters
Year:
2009

Citing 3
Cited 0

Channel-Optimized Quantization With Soft-Decision Demodulation for Space–Time Orthogonal Block-Coded Channels

IEEE Transactions on Signal Processing
Soft decoding for vector quantization over noisy channels with memory

IEEE Transactions on Information Theory
Quantization based on a novel sample-adaptive product quantizer (SAPQ)

IEEE Transactions on Information Theory

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Abstract

We design a channel optimized vector quantizer (COVQ) for symbol-by-symbol maximum a posteriori (MAP) hard-decision demodulated channels. The main objective is to exploit the non-uniformity of the indices representing the quantized source via the MAP decoder and iteratively optimize the overall discrete channel (at the symbol level) jointly with the quantizer. We consider memoryless Gaussian and Gauss-Markov sources transmitted over a binary phase-shift keying modulated Rayleigh fading channel. Our scheme has less encoding computational and storage complexity (particularly for noisy channel conditions) than both conventional and soft-decision COVQ systems, which use hard-decision and soft-decision maximum likelihood demodulation, respectively. Furthermore, it provides a notable signal-to-distortion ratio gain over the former system, and in some cases it matches or outperforms the latter one.