Sparse Channel Estimation for OFDM Systems Based on Sparse Reconstruction by Separable Approximation
In high rate data orthogonal frequency division multiplex (OFDM) communication systems, many encountered channels trend to have the structure of sparse multipath. The systems over multipath channels usually require that the channel response be known at the receiver and thus channel estimation is required. In this paper, a novel scheme based on the typical least square (LS) and sparse reconstruction by separable approximation (SpaRSA) for the sparse channel estimation is presented to improve the poor performance of the LS and l2 norm channel estimations. This proposed scheme can reach the global optimal solution and leads to superior channel estimation performance by applying LS estimates and introducing the noise effect into the regularization parameter in the SpaRSA algorithm. And the simulation results show the validity of the proposed approach.
2016 International Conference on Information System and Artificial Intelligence (ISAI)