Generalized Optimal Step-Size for Blind Multichannel LMS System Identification

Abstract

The choice of step-size in adaptive blind channel identification using the multichannel least mean squares (MCLMS) algorithm is critical and controls its convergence rate, stability, and sensitivity to noise. In this letter, we derive the expression for an optimal step-size in the Wiener sense and investigate its properties. An implementation technique for the Wiener solution of the self-adaptive step-size is presented, and it is shown that significant performance improvements are obtained compared to existing approaches in the presence of noise