ADAPTIVE BEAMFORMING USING GENERALIZED TRIGONOMETRIC TRANSFORM DOMAIN LMS ALGORITHM

Abstract

In the recent years, the use of wireless technology has grown rapidly. This has led to increase in number of users and services. To meet Quality of Service (QoS) standards in reception and transmission and track a wider coverage area keeping in mind limited spectrum available, spatial processing was devised as a practical solution. Spatial processing technique is used to discriminate two signals on the basis of location with respect to an array of antennas using the knowledge of the attributes of the signal. In a multiple access system, some users may occupy the same frequency bands allotted to them, thus frequency domain filtering techniques cannot be used to separate signals coming from these users. Beamformers employ spatial processing technique to estimate the location of the desired and interfering signals and adjust their reception radiation pattern to provide maximum amplitude in the direction of desired signals and maximum rejection in the direction of interfering signals in the presence of Gaussian noise. Adaptive Algorithms are used to adjust the radiation pattern by tuning the weights of an antenna array. The most commonly used algorithm for tuning the weights is Least Mean Sqaures algorithm. It is least computationally complex but it suffers from slow convergence of the beamformer output to the desired output. Many alternatives have been devised in the past. One such alternative is using Discrete Cosine Transform and Discrete Fourier Transform to decorrelate the input data which leads to faster convergence. In this thesis, generalized transform architecture is proposed which uses these transforms as well as Discrete Sine Transform and Discrete Hartley Transform and employed with conventional LMS algorithm to study the convergence of beamformer output using these transforms. Mathematical analysis of Least Mean Square Algorithm and Transform Domain Least Mean Square Algorithm has been discussed. Both of these algorithms have been implemented in MATLAB and LabVIEW and the results have been analysed with varying input correlation parameters.