A data reusage algorithm based on incremental combination of LMS filters

This work introduces a new data reuse algorithm based on the incremental combination of LMS filters. It is able to outperform the Affine Projection Algorithm (APA) in its standard form, another well-known data reuse adaptive filter. First, the so called true gradient data reuse LMS-sometimes referred to as data reuse LMS-is shown to be a limiting case of the regularized APA. Afterwards, an incremental counterpart of its recursion is inspired by distributed optimization and adaptive networks scenarios. Simulations in different scenarios show the efficiency of the proposed data reuse algorithm, that is able to match and even outperform the APA in the mean-square sense at lower computational complexity.