Scheduled-Stepsize NLMS Algorithm

Abstract

This paper presents a method of scheduling stepsizes for the normalized least-mean-squares (SS-NLMS) algorithm. Geometrically interpreting the mean square deviation (MSD) learning curve leads to establishing an objective curve and to constructing a lookup table of stepsizes in order for the MSD to follow the curve. The SS-NLMS shows not only good performance but also robustness with respect to different signal-to-noise ratio (SNR) in measurement noise and different correlation in input signals with a very small number of online computations. Moreover, the scalability of the tabled stepsize with respect to the number of taps is described. For the efficient memory usage in practice, a modified version replaces the tabled stepsizes by down-sampled stepsizes with no performance degradation.