Design of iRLS Algorithm With/Without Pre-Filter for Antenna Beam Forming Technique

Abstract

Beamforming is one of the most significant processes in smart antennas. To change the antenna beam pattern is the major function of beamforming for a given angle. The algorithm that is adaptive beamforming is utilized to choose the conventional weights of each array element from acquired data of array antenna to extract the desired source signal while cancelling noise and interference. A lot of algorithms are already existing for antenna beamforming technique but they all experience low convergence. So this paper deals with an iRLS algorithm for antenna beamforming technique with Particle Swarm Optimized (PSO) FFT filter. As a result, antenna beamforming based on iRLS shows fast convergence with reduced design complexity. The overall work is simulated in MATLAB. The parameters like amplitude, bit error rate, capacity, SINR and error function values are evaluated. Our work is compared with existing Applebaum, Recursive Least Squares (RLS) (with/without filter) and Least Mean Square (LMS) algorithm. Fast convergence is occurred by iRLS when compared with existing algorithms. The iRLS is converged at 90th iteration, whereas existing algorithms likewise RLS with pre-filter, RLS without pre-filter, LMS and Applebaum is converged at 200, 400, 600 and 850th iteration. So here, our proposed iRLS gives better performance when compared with others.