Adaptive Blind Identification of Sparse SIMO Channels using Maximum a Posteriori Approach
Nacerredine Lassami, Abdeldjalil Aïssa El Bey, Karim Abed‐Meraim · 2018 52nd Asilomar Conference on Signals, Systems, and Computers · 2018
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 ℓpnorm 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.