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|Title:||A mutual information approach for comparing LLR metrics for iterative decoders||Authors:||Zhang, J.
|Issue Date:||2009||Citation:||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  and the two-symbol-observation-interval LLR metric in . 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|
|Appears in Collections:||Staff Publications|
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