A novel gain distribution policy based on individual-coefficient convergence for PNLMS-type algorithms

Abstract

This paper introduces a new gain distribution policy for proportionate normalized least-mean-square (PNLMS)-type algorithms. In the proposed approach, gains assigned to the coefficients that have achieved the vicinity of their optimal values are transferred to other coefficients. To estimate such a vicinity, a metric based on the variation rate of the adaptive filter coefficient magnitude is devised, which is used as a way for assessing the individual-coefficient convergence. Then, the proposed approach is applied to the PNLMS, improved PNLMS (IPNLMS), and individual-activation-factor PNLMS (IAF-PNLMS), leading to enhanced versions of these algorithms. Simulation results show that the proposed approach (and the corresponding enhanced algorithms) performs well for different operating scenarios.