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No description available.

Consider the problem of designing capacity-achieving codes for multi-terminal communication scenarios. For point-to-point communication problems, one can optimize a single code to approach capacity, but for multi-terminal problems this translates to optimizing a single code to perform well over the entire region of channel parameters. A coding scheme is called universal if it allows reliable communication over the entire achievable region promised by information theory. It was recently shown that terminated low-density parity-check convolutional codes (also known as spatially-coupled low-density parity-check ensembles) have belief-propagation thresholds that approach their maximum a-posteriori thresholds. This phenomenon, called "threshold saturation via spatial-coupling," was proven for binary erasure channels and then for binary memoryless symmetric channels. This approach provides us with a new paradigm for constructing capacity approaching codes. It was also conjectured that the principle of spatial coupling is very general and that the phenomenon of threshold saturation applies to a very broad class of graphical models. In this work, we consider a noisy Slepian-Wolf problem (with erasure and binary symmetric channel correlation models) and the binary-input Gaussian multiple access channel, which deal with correlation between sources and interference at the receiver respectively. We derive an area theorem for the joint decoder and empirically show that threshold saturation occurs for these multi-user scenarios. We also show that the outer bound derived using the area theorem is tight for the erasure Slepian-Wolf problem and that this bound is universal for regular LDPC codes with large left degrees. As a result, we demonstrate near-universal performance for these problems using spatially-coupled coding systems.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1994. / Includes bibliographical references (leaves 177-182). / Microfiche. / xvii, 182 leaves, bound ill. 29 cm

Thesis (PhD) -- University of South Australia, 1999

This thesis addresses the problem of decoding high rate convolutional codes directly without resorting to puncturing. High rate codes are necessary for applications which require high bandwidth efficiency, like high data rate communication systems and magnet recording systems. Convolutional (rate k/n) codes, used as component codes for turbo codes, are preferred for their regular trellis structure and the resulting ease in decoding. However, the branch complexity of the (primal) code trellis increases exponentially with k for k/(k+1) codes, making decoding on the code trellis quickly impractical with increasing code rate. 'Puncturing' is the method traditionally used for generating high rate codes, which keeps the decoding complexity nearly the same for a wide range of code rates, since the same ?mother? code decoder is used at the receiver, while only the puncturing and depuncturing pattern is altered for changes in code rate. However, 'puncturing' puts a constraint in the search for the best possible high rate code, thereby resulting in a performance penalty, particularly at high SNRs.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Also available in print.

Thesis (Ph.D.)--University of Delaware, 2008. / Principal faculty advisor: Xiang-Gen Xia, Dept. of Electrical & Computer Engineering. Includes bibliographical references.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 118-125). Also available in electronic version.

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaf 24). Also available on the World Wide Web.