Blind Identification of Sparse SIMO Channels using Maximum A Posteriori Approach
Abdeldjalil Aïssa El Bey, Karim Abed‐Meraim · 2009
In this paper, we are interested in blind identification of sparse single-input multiple-output (SIMO) systems. A maximum a posteriori approach is considered using generalized Laplacian distribution for the channel coef-ficients. This leads to a cost function given by the deter-ministic maximum likelihood (ML) criterion penalized by ‘a sparsity measure ’ term expressed by the `p norm of the channel coefficient vector. A simple but efficient optimization algorithm using gradient technique with optimal step-size is proposed. The simulations show that the proposed method outperforms the ML technique in terms of estimation error and is robust against channel order overestimation errors. 1.