A variable-step-size NLMS algorithm using statistics of channel response

This paper proposes a new variable-step-size control for the normalized least-mean-square (NLMS) algorithm. A step-size vector is used, with a different value for each adaptive weight. With prior knowledge of the channel impulse response statistics, the optimal step-size vector is obtained by minimizing the mean-square deviation (MSD) between the optimal and estimated filter coefficients. In addition, filter convergence is proved and the relationship between the proposed and existing algorithms are analyzed. The proposed method achieves better steady-state performance compared to existing algorithms. The effectiveness of the proposed algorithm is demonstrated through computer simulations.