# Adaptive Blind Identification of Sparse SIMO Channels using Maximum a Posteriori Approach

1 Lab-STICC_IMTA_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : In this paper, we are interested in adaptive blind channel identification of sparse single input multiple output (SIMO) systems. A generalized Laplacian distribution is considered to enhance the sparsity of the channel coefficients with a maximum a posteriori (MAP) approach. The resulting cost function is composed of the classical deterministic maximum likelihood (ML) term and an additive $\ell_p$ norm of the channel coefficient vector which represents the sparsity penalization. The proposed adaptive optimization algorithm is based on a simple gradient step. Simulations show that our method outperforms the existing adaptive versions of cross-relation (CR) method.
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Communication dans un congrès
Asilomar Conference on Signals, Systems, and Computers, Oct 2018, Pacific Grove, Ca, United States. Proceedings Asilomar Conference on Signals, Systems, and Computers, 2018

https://hal.archives-ouvertes.fr/hal-01847560
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Soumis le : lundi 23 juillet 2018 - 16:07:41
Dernière modification le : mercredi 19 décembre 2018 - 15:26:07
Document(s) archivé(s) le : mercredi 24 octobre 2018 - 15:41:14

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• HAL Id : hal-01847560, version 1

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Nacerredine Lassami, Abdeldjalil Aissa El Bey, Karim Abed-Meraim. Adaptive Blind Identification of Sparse SIMO Channels using Maximum a Posteriori Approach. Asilomar Conference on Signals, Systems, and Computers, Oct 2018, Pacific Grove, Ca, United States. Proceedings Asilomar Conference on Signals, Systems, and Computers, 2018. 〈hal-01847560〉

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