Block-sparsity-aware LMS algorithm for network echo cancellation
Network echo path impulse response is single-block-sparse in nature. In order to obtain a single-block-sparse estimate of the unknown echo path, a new least mean squares (LMS) algorithm is proposed by introducing the penalty of single block sparsity, which is the difference between the mixed norm and norm of the uniformly partitioned filter tap-weight vector, into the original mean-square-error cost function. This is motivated by the fact that the difference between the mixed norm and norm of a vector is minimised only when there is at most one non-zero block in the vector. Numerical simulation results show that the proposed algorithm can effectively estimate and track the unknown echo path, outperforming existing block-sparsity-induced LMS algorithms.
