Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICC.2009.5198635
Title: A mutual information approach for comparing LLR metrics for iterative decoders
Authors: Zhang, J. 
Armand, M.A. 
Pooi, Y.K. 
Issue Date: 2009
Source: Zhang, J.,Armand, M.A.,Pooi, Y.K. (2009). A mutual information approach for comparing LLR metrics for iterative decoders. IEEE International Conference on Communications : -. ScholarBank@NUS Repository. https://doi.org/10.1109/ICC.2009.5198635
Abstract: We develop an approach to compare different log-likelihood ratio (LLR) metrics for iterative soft decoding. We show that an LLR metric for a function of the received signals is a sufficient statistic to this function about the binary channel input. We also prove that when the function belongs to a set of specific mappings, the corresponding LLR metric can feed the maximal mutual information to the decoder. For decoding low density parity check codes with the belief-propagation decoder, we develop a method to estimate the minimal average number of iterations. The results are applied to compare the Gaussian metric in [1] and the two-symbol-observation-interval LLR metric in [2]. The latter is shown to be superior. ©2009 IEEE.
Source Title: IEEE International Conference on Communications
URI: http://scholarbank.nus.edu.sg/handle/10635/51087
ISBN: 9781424434350
ISSN: 05361486
DOI: 10.1109/ICC.2009.5198635
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