Performance evaluation of the fast euclidean direction search algorithm for adaptive beamforming applications

This paper presents a new study of the performance evaluation of Fast Euclidean Direction Search (FEDS) adaptive beamforming algorithm for mobile communications application. The performance evaluation focuses on the effect of window length parameter (L) of the FEDS algorithm on the FEDS performance in terms of interference suppression capability, Mean Square coefficients Deviation (MSD), and Mean Square Error (MSE). The performance evaluation was evaluated in an Additive White Gaussian Noise (AWGN) model. Moreover, the performance evaluation was carried out using other adaptive algorithms beside the FEDS. These are LMS, NLMS, and RLS algorithms. The simulation results of adaptive beamforming with eight elements in the array showed that the best window length parameter (L) is ten and when the window length parameter (L) has increased more than ten or less, then the performance begins to deteriorate. In addition, the FEDS had better performance in terms of interference suppression capability, minimum Mean Square coefficients Deviation (MSD) and minimum Mean Square Error (MSE) compared with LMS, NLMS algorithms and similar to the RLS algorithm.