Performance Analysis of Adaptive Array Signal Processing Algorithms

Adaptive antenna is an array of antenna elements with signal processing capability to optimize its radiation pattern in response to the changing signal environment. The adaptive antenna improves the performance of wireless communication by increasing channel capacity and spectrum efficiency, and reduces the cost for establishing new wireless networks. Adaptive antenna aims at increasing the gain in the direction of desired user and direct nulls in the direction of interfering signals. It involves processing of signals induced on an array of antennas that can estimate the direction of radiating sources and calculate optimum weights for adaptive beamforming. This paper presents a performance evaluation of different direction of arrival (DOA) estimation and adaptive beamforming (ABF) algorithms. The simulation results show that multiple signal classification (MUSIC) algorithm provides more accurate and stable results among other DOA estimation techniques while recursive least square (RLS) algorithm shows the fastest convergence rate among other beamforming algorithms. In order to reduce the computation complexity and to increase the convergence speed, the problem of DOA estimation and ABF are approached as a non-linear mapping which can be modelled using a suitable neural network. Artificial neural intelligence provides best solution for real-time implementation of beamforming algorithms in practical systems. The capabilities of adaptive antenna are easily employable to cognitive radio and orthogonal frequency division multiple access (OFDMA) system.