A new variable step-size block LMS algorithm for a non-stationary sparse systems
The conventional LMS algorithm has been successfully used in adaptive filtering for system identification (SI) problem. In telecommunications, acoustic echo SI problems usually have relatively large filter lengths that take a long time to be estimated. To overcome this problem, the block least-mean-square algorithm (BLMS) has been proposed. In BLMS, the filter coefficients are updated for blocks of input instead of each sample of input data. Using this advantage, we propose a new block-LMS algorithm with a function controlled variable step-size LMS (FC-VSSLMS) for non-stationary sparse systems identification. The performance of proposed algorithm is compared to those of the original BLMS and reweighted zero-attracting block LMS (RZA-BLMS), in terms of convergence rate and mean-square-deviation (MSD) in additive white Gaussian noise (AWGN) and additive uniformly distributed noise (AUDN). Simulations show that the proposed algorithm has a better performance than those of the other algorithms.