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An improved zero-attracting normalized least mean square algorithm for sparse channel estimation

An improved zero-attracting normalized least mean square (IZA-NLMS) algorithm is proposed for sparse channel estimation. The proposed algorithm is realized by using the error sequence to design the step-size of the sparse-aware normalized least mean square (NLMS) algorithms. Also, the computational complexity reduction strategy is used in the proposed IZA-NLMS algorithm. Computer simulations are constructed to verify the performance of the proposed IZA-NLMS algorithm over a sparse multi-path channel. The simulation results show that the proposed IZA-NLMS algorithm provides faster convergence speed and lower mean square error in comparison with the previously proposed sparse-aware NLMS algorithms.