From Maximum Likelihood to Iterative Decoding

Abstract : Iterative decoding is considered in this paper from an optimization point of view. Starting from the optimal maximum likelihood decoding, a (tractable) approximate criterion is derived. The global maximum of the approximate criterion is analyzed: the maximum likelihood solution can be retrieved from the approximate criterion in some particular cases. The classical equations of turbo-decoders can be obtained as an instance of an hybrid Jacobi/Gauss-Seidel implementation of the iterative maximization for the tractable criterion. The extrinsics are a natural consequence of this implementation. In the simulation part, we show a practical application of these results.
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Florence Alberge, Ziad Naja, Pierre Duhamel. From Maximum Likelihood to Iterative Decoding. ICASSP 2011, May 2011, Prague, Czech Republic. pp.3052-3055, ⟨10.1109/ICASSP.2011.5946302⟩. ⟨hal-00617262⟩

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