Adaptive Smart Antenna using Neural Network (SMI Algorithm)

Abstract

Smart antenna systems are of great importance in wireless communications and RADAR applications ,They effectively enhance the system capacity and reduce the co-channel interference. Smart antenna is an array antenna that uses adaptive beam forming algorithms to steer the main beam towards the desired signal direction and reject the interfering signals of the same frequency from other direction without moving the antenna. This is achieved by continuously updating the weights of each radiating element (antenna). An algorithm with low complexity, low computation cost, high speed convergence rate and better performance is usually preferred. This paper introduces a new performance investigation and comparison between five different beam forming algorithms : Least Mean Square(LMS), Normalised Least Mean Square(NLMS),Sample Matrix Inversion(SMI),Recursive Least Square(RLS) and Hybrid Least Mean Square/ Sample Matrix Inversion (LMS/SMI). In this investigations, the number of array element and the displacement among them are changed in each algorithm is optimized and demonstrated using MATLAB software package.