A Data-Reuse Approach for an Optimized LMS Algorithm

Abstract

The least-mean-square (LMS) type algorithms are widely spread in signal processing, especially in the system identification context. The classic LMS algorithm has major drawbacks due to the fixed step-size that limits the overall performance. The optimized LMS (LMSO) algorithm followed an optimization criterion and introduced a variable step-size so that it overcomes the drawbacks of the LMS algorithm. Some scenarios where the unknown system changes have highlighted the need for the LMSO algorithm to improve how fast it models the new system. In this paper, we apply the data-reuse approach for the LMSO algorithm aiming to increase the convergence rate. The simulations outline the performance improvement for the data-reuse method in combination with the LMSO algorithm.