Channel quantizers that maximize random coding exponents for binary-input memoryless channels

The problem of finding the optimum output quantizer for a given discrete memoryless channel is investigated, where the quantizer output has fewer values than the channel output. While mutual information has received attention as an objective function for optimization, the focus of this paper is use...

Full description

Saved in:
Bibliographic Details
Main Authors: Yagi, H., Kurkoski, B. M.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Complexity theory
Encoding
Heuristic algorithms
Linear programming
Mutual information
Quantization
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The problem of finding the optimum output quantizer for a given discrete memoryless channel is investigated, where the quantizer output has fewer values than the channel output. While mutual information has received attention as an objective function for optimization, the focus of this paper is use of the random coding exponent, which was originally derived by Gallager, as criteria. Two problems are addressed, where one problem is a partial problem of the other. The main result is a quantizer design algorithm, and a proof that it finds the optimum quantizer in the partial problem. The quantizer design algorithm is based on a dynamic programming approach, and is an extension of a mutual-information maximization method. For the binary-input case, it is shown that the optimum quantizer can be found with complexity that is polynomial in the number of channel outputs.
ISSN:1550-3607
1938-1883
DOI:10.1109/ICC.2012.6363931