A reweighted l0-norm-constraint LMS algorithm for sparse system identification

The l0-norm-constraint algorithm is widely used in sparse system identification due to its attractive performance. However, the algorithm is sensitive to the tuning parameters and its convergence speed can be further improved due to the small attraction range of the zero attractor. This paper proposes a reweighted l0-norm-constraint Least Mean Square (l0-RLMS) algorithm which expands the attraction range of the zero attractor to accelerate the convergence with even lower mean-square deviation (MSD) value and lower sensitivity to the tuning parameters. The theoretical analysis of the proposed algorithm, along with numerical simulations and comparisons with the latest sparse algorithms, is carried out. The analysis and simulations show that the l0-RLMS algorithm has lower steady-state MSD, lower sensitivity of tuning parameters and lower complexity than the l0-norm-constraint algorithm.