Blind identification of sparse SIMO channels using maximum a posteriori approach

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 coefficients. This leads to a cost function given by the deterministic maximum likelihood (ML) criterion penalized by ‘a sparsity measure’ term expressed by the ℓ
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.