Normalized least mean square-based adaptive sparse filtering algorithms for estimating multiple-input multiple-output channels

This paper studies normalized least mean square-based adaptive sparse filtering algorithms for estimating multiple-input multiple-output (MIMO) channels. Although the MIMO channel is often modeled as sparse, traditional normalized least mean square-based filtering algorithm never takes the advantage of the inherent sparse structure information and thus causes some performance loss. Unlike the traditional method, the proposed two adaptive sparse channel estimation methods exploit the sparse structure information of MIMO channels. To validate the effectiveness of proposed MIMO channel estimates, theoretical analysis and simulation results are provided. We derive steady-state mean-square deviations of the proposed MIMO channel estimates and theoretically show that it is better than the traditional one. Moreover, their performance advantages are confirmed by computer simulations.