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1 Quantization of Log-Likelihood Ratios to Maximize Mutual Information Wolfgang
BibTeX
@MISC{_1quantization,
author = {},
title = {1 Quantization of Log-Likelihood Ratios to Maximize Mutual Information Wolfgang},
year = {}
}
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Abstract
Abstract—We propose a quantization scheme for log-likelihood ratios which optimizes the trade-off between rate and accuracy in the sense of rate distortion theory: as distortion measure we use mutual information to determine quantization and decision levels maximizing mutual information for a given rate over a Gaussian channel. This approach is slightly superior to the previously proposed idea of applying the Lloyd-Max algorithm to the ’soft bit ’ density associated to the L-values. A further data rate reduction can be achieved with entropy coding, because the optimum quantization levels based on mutual information are used with pronounced unequal probabilities. Index Terms—Mutual information, soft bits, quantization, entropy coding, iterative decoding
Keyphrases
log-likelihood ratio mutual information mutual information wolfgang entropy coding iterative decoding quantization scheme lloyd-max algorithm soft bit soft bit density decision level distortion measure index term mutual information data rate reduction rate distortion theory gaussian channel pronounced unequal probability optimum quantization level
