<p> This thesis launches some new inquires and makes significant progress in the active
research areas of joint source-channel coding and multiple description coding. Two
interesting but previously untreated problems are investigated and partially settled:
1) can index assignment of source codewords be optimized with respect to a given
joint source-channel decoding scheme, and if so, how? 2) can joint source-channel
coding be optimized with respect to a given multiple description code, and if so,
how?</p> <p> The first problem is formulated as one of quadratic assignment. Although quadratic assignment is NP-hard in general, we are able to develop a near-optimum index assignment algorithm for joint source-channel (JSC) maximum a posteriori (MAP)
decoding, if the input is a Gaussian Markov sequence of high correlation. For general
cases, good heuristic solutions are proposed. Convincing empirical evidence is
presented to demonstrate the performance improvement of the index assignments
optimized for MAP decoding over those designed for hard-decision decoding.</p> <p> The second problem is motivated by applications of signal communication and estimation in resource-constrained lossy networks. To keep the encoder complexity at a minimum, a signal is coded by a multiple description quantizer (MDQ) without channel coding. The code diversity of MDQ and the path diversity of the network are exploited by decoders to combat transmission errors. A key design objective is resource scalability: powerful nodes in the network can perform JSC-MD estimation under the criteria of maximum a posteriori probability or minimum mean-square error (MMSE), while primitive nodes resort to simpler multiple description (MD) decoding, all working with the same MDQ code. The application of JSC-MD to distributed estimation of hidden Markov models in a sensor network is demonstrated. The proposed JSC-MD MAP estimator is an algorithm of the longest path in a weighted directed acyclic graph, while the JSC-MD MMSE decoder is an extension of the well-known forward-backward algorithm to multiple descriptions. They outperform the existing
hard-decision MDQ decoders by large margins.</p> / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/17393 |
| Date | 01 1900 |
| Creators | Wang, Xiaohan |
| Contributors | Wu, Xiaolin, Dumitrescu, Sorina, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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