Complexity considerations for transform-domain adaptive filters

Abstract

This paper is concerned with the computational complexity and convergence performance of transform-domain adaptive filtering algorithms. In particular, the transform-domain least-mean-square algorithm and the generalized subband decomposition LMS algorithm are considered. Reduced complexity variants of these algorithms are developed based on the concept of selective partial updating. The effect of power normalization on the computational complexity is analyzed, and an alternative implementation is proposed to reduce the number of divisions to one. This implementation is also shown to be the only realization that lends itself to complexity reduction by selective partial updating. The complexity and performance of the algorithms are illustrated in acoustic echo cancellation and channel equalization applications by way of computer simulations.