Optimal Step-Size Affine Projection Algorithm

Abstract

This letter describes how to set up the step size of the affine projection algorithm (APA) based on mean-square deviation analysis. The analysis considers the cross-correlation between the current weight error vector and the prior measurement noises associated with the reused inputs vectors for better prediction of the learning behavior of the APA. With the predetermined step size based on the analysis, the proposed approach eliminates the parameter-tuning process and the derived algorithm achieves both the fast convergence rate and the low steady-state error. Simulation results show that the proposed algorithm performs better than previous algorithms.