Computationally-Efficient DNLMS-Based Adaptive Algorithms for Echo Cancellation Application

This paper investigates the application of the delayed normalized least mean square (DNLMS) algorithm to echo cancellation. In order to reduce the amount of computations, DNLMS is modified by using computationallyefficient techniques including the M-Max algorithm, a Stopand-go (SAG) algorithm, and Power-of-two (POT) quantization. For the SAG algorithm, a new stopping criterion related to the regressor energy is presented. Cumulatively, these modifications lead to reductions in power and/or area. Simulation results and comparisons with the normalized least mean square (NLMS) algorithm are included to show the advantages of the computationally-efficient algorithms.