Sparsity-Information-Aided Least Mean Squares Method For Sparse Channel Estimation

A novel least mean squares (LMS) method that exploits sparsity level information for sparse channel estimation is presented and studied in this paper. This method utilizes the channel sparsity level information by incorporating a penalty term into the cost function and has better performance than the compared methods which do not take into account the sparsity level information. The convergence analysis of the proposed method is provided. Both the transient and the steady-state advantages of the proposed method are confirmed numerically. Simulation results indicate that the sparsity-information-aided LMS method has faster convergence and higher accuracy than the compared approaches when the channel sparsity level information is known.