A sparse function controlled variable step-size LMS algorithm for system identification

The recently proposed function controlled variable step-size least-mean-square (FCVSSLMS) algorithm has shown high performance. The performance of the algorithm can be improved further if the system is sparse. In this paper, we propose a new algorithm based on algorithm. The proposed algorithm imposes an approximate l₀-norm penalty in the cost function of the FCVSSLMS algorithm. The performance of the proposed algorithm is compared to those of the variable step-size LMS (VSSLMS) algorithm and FCVSSLMS algorithm in a system identification setting with an additive white Gaussian noise (AWGN). The proposed algorithm has shown high performance compared to the others in terms of convergence rate and mean-square-deviation (MSD).