Joint source-channel decoding in vector quantization over finite-state Markov channels and wireless channels

We investigate joint source-channel decoding for vector quantization over the finite-state Markov channel. In particular, we consider the decoding problem in which the channel state information is not available to the decoder. Based on hidden Markov modeling of the channel output, two decoding strat...

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Bibliographic Details
Main Authors: Yahampath, P., Pawlak, M.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Channel coding
Channel state information
Coding, codes
Decoding
Detection, estimation, filtering, equalization, prediction
Distortion
Exact sciences and technology
Fading
Gaussian channels
Hidden Markov models
Information, signal and communications theory
Sampling, quantization
Signal and communications theory
Signal, noise
Source coding
State estimation
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Transmission and modulation (techniques and equipments)
Vector quantization
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Summary:We investigate joint source-channel decoding for vector quantization over the finite-state Markov channel. In particular, we consider the decoding problem in which the channel state information is not available to the decoder. Based on hidden Markov modeling of the channel output, two decoding strategies are proposed. The first one is a soft-decoder which estimates the source reconstruction vectors directly from the observed channel output samples. The second one is a hard-decoder based on joint MAP estimation of channel symbols and channel states. Application of these decoders to wireless channels with multipath fading is also investigated. Simulation results are provided for a flat fading wireless channel and a Gauss-Markov source.
DOI:10.1109/GLOCOM.2004.1377912