| Summary: | 博士 === 國立交通大學 === 電信工程系 === 91 === This study investigated the joint source-channel coding techniques for use with vector quantization (VQ) over channels with memory. Most previous research concentrated on memoryless binary symmetric channels, despite evidence showing that transmission errors encountered in indoor and outdoor wireless channels exhibit various degrees of statistical dependencies. To compensate for this shortage, the proposed joint source-channel coder design is based on Gilbert's two-state Markov chain model that better characterizes the observed error bursts. The first part of this study presents two encoding approaches that employ a Hadamard framework for analyzing VQ transmission over noisy channels. We proposed an index assignment algorithm in which pairwise swaps of VQ codevectors were arranged in accordance with Hadamard transform of channel transition probabilities. The decomposition of mapping vector indices becomes especially favorable when the complexity of searching the VQ for optimal indices is of primary concern. Also proposed is a new design approach to constrained VQ codebook design given by a linear mapping of a block code. The Hadamard transform proves effective in describing the VQ codebook, and use of it facilitates the search for a block code which better matches the expected channel condition. To optimize the mapping matrix to a given block code, we formulate its design as a combinatorial optimization problem that is amenable to the application of real-coded hybrid genetic algorithm. The second part of this study is concerned with the decoding algorithms having higher robustness against bursty channel errors. We developed a recursive algorithm for computing the a posteriori probability of a transmitted index sequence, and illustrated its performance in minimum mean-square error (MMSE) decoding of VQ data. The decoder is based on the Gilbert channel model that allows the exploitation of both intra-block and inter-block correlation of bit error sequences. Also proposed is a memory-enhanced convolutional decoder for use with a concatenated VQ/convolutional coding system. Simulation results indicated that with the aid of Gilbert channel characterization the joint source-channel coding algorithms can be developed to better track the intrinsic natures of channel errors.
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