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1 Lab-STICC TB CACS COM Lab-STICC - Laboratoire des sciences et techniques de l-information, de la communication et de la connaissance UMR 3192 2 SC - Département Signal et Communications 3 TSI - Département Traitement du Signal et des Images Institut Mines-Télécom-TELECOM ParisTech

Abstract : In this paper, we focus on the adaptive identification of sparse SIMO channels in a blind context. More specifically, we propose different adaptive implementations of the sparse cross relation SCR method then we compare and analyse their performances in terms of convergence rate, estimation accuracy and robustness. The SCR method proceeds as follows: at first a blind approach based on the cross-relation criterion is derived for channel estimation. Secondly, to take into account the channel sparsity, the criterion is penalized by adding an extra lp norm term in order to enforce the sparsity of the desired solution. The corresponding algorithm i.e. SCR is shown to outperform the original CR method in terms of estimation accuracy and robustness to channel order over-estimation errors. The adaptive versions of the SCR proposed in this paper are shown to preserve the main advantages of the batch technique but suffer from low convergence rate for large dimensional systems.

Keywords : Sparse channel SIMO Identification aveugle

Autor: Abdeldjalil Aissa El Bey - Karim Abed-Meraim - Christophe Laot -



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