ON DATA-REUSE ADAPTIVE ALGORITHMS

Adaptive filters play an important role in signal processing and several different categories appear in the literature. In this paper, we discuss a class of adaptive algorithms called data-reuse. The hope of a data-reuse adaptive algorithm is to improve the convergence rate of the initial algorithm. We show, in particular, that the so-called block exact normalized LMS is a block version of the Schnaufer and Jenkins data-reuse NLMS, and show that many different forms of the latter exist but will not improve convergence or reduce complexity (compared to the initial form). We also discuss the structure and efficiency of these algorithms.

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