On some Properties of the Mutual Information between Extrinsics with Application to Iterative Decoding

International audienceIterative decoding is an efficient error-correction tool based on the exchange of extrinsic probabilities between the constituent decoders. In this paper, the properties of the mutual information between the extrinsic LLR at the output of two constituent decoders are analyzed with application to turbo and LDPC codes. This is a bridge between information-theoretic analysis and practical implementations. It is proved here that the mutual information between extrinsics is a lower bound of the mutual information between each extrinsic and the transmitted message. In addition, an efficient online evaluation is provided in the paper with accuracy validated through numerical experiments. As an application, the mutual information between extrinsics is used for designing efficient stopping criterion and error detection rules at the decoder side. Two online methods for the estimation of optimal scaling factor to be applied to the extrinsic LLR are also derived. In contrast with most references, an analytical expression is obtained that does not require estimation of the actual transmitted bits. All results in the paper are derived for Gaussian distributed LLR with independent mean and variance

Méthodes de codage et d'estimation adaptative appliquées aux communications sans fil

Les recherches et les contributions présentées portent sur des techniques de traitement du signal appliquées aux communications sans fil. Elles s’articulent autour des points suivants : (1) l’estimation adaptative de canaux de communication dans différents contextes applicatifs, (2) la correction de bruit impulsionnel et la réduction du niveau de PAPR (Peak to Average Power Ratio) dans un système multi-porteuse, (3) l’optimisation de schémas de transmission pour la diffusion sur des canaux gaussiens avec/sans contrainte de sécurité, (4) l’analyse, l’interprétation et l’amélioration des algorithmes de décodage itératif par le biais de l’optimisation, de la théorie des jeux et des outils statistiques. L’accent est plus particulièrement mis sur le dernier thème