Novelty Study of the Window Length Effects on the Adaptive Beam-forming Based-FEDS Approach

Abstract

To date, there are very few researches on performance investigation or application of adaptive beam-forming that is based on the Fast Euclidean Direction Search (FEDS) algorithm. Correspondingly, one of our primary goals in this paper will be to introduce a novelty study on the impact of selecting the iteration number and the window length (L) on the overall performance of FEDS-based beam-forming approach. The communication channel is implemented for multipath Rayleigh fading model with different paths numbers, delays, Doppler shift frequencies and gains. For comparison, this study considers different benchmark approaches which are Recursive Least Square (RLS), Least Mean Square (LMS), and Normalized LMS (NLMS). One of the important findings, based on the simulation results, indicates that the best window length (L) should be more than the number of the elements in the array. This is a necessary condition to obtain significant improvement in the performance of FEDS compared with both LMS and NLMS algorithms and slight improvement compared with RLS algorithm