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

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.
Type de document :
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
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01847560
Contributeur : Bibliothèque Télécom Bretagne <>
Soumis le : lundi 23 juillet 2018 - 16:07:41
Dernière modification le : mercredi 6 mars 2019 - 15:10:49
Document(s) archivé(s) le : mercredi 24 octobre 2018 - 15:41:14

Fichier

adaptive-blind-identification....
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01847560, version 1

Citation

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〉

Partager

Métriques

Consultations de la notice

93

Téléchargements de fichiers

59