A PNLMS Algorithm With Individual Activation Factors

Abstract

This paper presents a proportionate normalized least-mean-square (PNLMS) algorithm using individual activation factors for each adaptive filter coefficient, instead of a global activation factor as in the standard PNLMS algorithm. The proposed individual activation factors, determined in terms of the corresponding adaptive filter coefficients, are recursively updated. This approach leads to a better distribution of the adaptation energy over the filter coefficients than the standard PNLMS does. Thereby, for impulse responses exhibiting high sparseness, the proposed algorithm achieves faster convergence, outperforming both the PNLMS and improved PNLMS (IPNLMS) algorithms.