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Abstract: Iterative decoding was not originally introduced as the solution to anoptimization problem rendering the analysis of its convergence very difficult.In this paper, we investigate the link between iterative decoding and classicaloptimization techniques. We first show that iterative decoding can be rephrasedas two embedded minimization processes involving the Fermi-Dirac distance.Based on this new formulation, an hybrid proximal point algorithm is firstderived with the additional advantage of decreasing a desired criterion. In asecond part, an hybrid minimum entropy algorithm is proposed with improvedperformance compared to the classical iterative decoding. Even if this paperfocus on iterative decoding for BICM, the results can be applied to the largeclass of turbo-like decoders.

Author: Florence Alberge, Ziad Naja, P. Duhamel



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