Selective-partial-update proportionate normalized least-mean-square adaptive filtering for network echo cancellation

Abstract

A reduced-complexity, fast converging adaptive filter may be used for network echo cancellation applications, including applications having sparse echo paths. The new filter, referred to as a selective-partial-update proportionate NLMS filter, may be based on a proportionate NLMS (PNLMS) technique and selective partial updating of the adaptive filter coefficients. The new PNLMS filter may exploit sparseness of a communications channel to speed up the initial convergence of the NLMS technique included in the filter by weighting regressor data proportionately with an estimated magnitude of the channel impulse response. Selective partial updating is essentially a data selection method to reduce the computational complexity. The performance of the selective-partial-update PNLMS filter compares favorably to an adaptive filter using standard PNLMS for echo paths specified in ITU-T Recommendation G.168. Further, the selective-partial-update PNLMS filter maintains the convergence speed of a standard PNLMS filter at a reduced computational complexity.