Variable is good: Adaptive sparse channel estimation using VSS-ZA-NLMS algorithm

Broadband wireless communication often requires accurate channel state information (CSI) at the receiver side due to the fact that broadband channel is described well by sparse channel model. To exploit the channel sparsity, invariable step-size zero-attracting normalized least mean square (ISS-ZA-NLMS) algorithm was applied in adaptive sparse channel estimation (ASCE). However, ISS-ZA-NLMS cannot trade off the algorithm convergence rate, estimation performance and computational cost. In this paper, we propose a variable step-size ZA-NLMS (VSS-ZA-NLMS) algorithm to improve the adaptive sparse channel estimation in terms of bit error rate (BER) and mean square error (MSE) metrics. First, we derive the proposed algorithm and explain the difference between VSS-ZA-NLMS and ISS-ZA-NLMS algorithms. Later, to verify the effectiveness of the proposed algorithm, several selected computer simulation results are shown.