Adaptive beamforming in wireless communications

ABSTRACT ADAPTIVE BEAMFORMING IN WIRELESS COMMUNICATIONS Çakır, Oğuzhan M.S., Department of Electrical and Electronics Engineering Supervisor: Asst. Prof. Dr. Aysel ŞAFAK September, 2003 In this thesis, we have investigated the benefit of the adaptive beamforming approach as a solution to improve the performance and the capacity of the wireless communication systems. In this study, an adaptive beamformer with uniform linear array (ULA) geometry is studied and its performance is evaluated by computer simulations. We also describe techniques for implementing adaptive beamforming with the Least Mean Squares (LMS), Recursive Least Squares (RLS) and, Direct Sample Covariance Matrix Inversion (DMI) adaptive arrays. The DMT approach can be shown to converge more rapidly than the LMS algorithm, the practical difficulties that are associated with the DMI algorithm approach should be appreciated. It has two major problems : (1) increased computational complexity that can not be easily overcome through the use of VLSI, and (2) numerical instability resulting from the use of finite- precision arithmetic and the requirement of inverting a large matrix. A comparison made between the diversity systems and adaptive arrays has shown that a marked improvement in performance and capacity of wireless systems can be obtained. We use analytical and computer simulation techniques to determine the performance of adaptive arrays when the received desired and interfering signals are subject to Rayleigh/Rician fading. Results show that adaptive beamforming schemes is significantly better than maximal ratio combining even when the number of interferers is greater than the number of antennas. Results for wireless systems show that adaptive beanrforming increases the output signal-to-interference ratio at the receiver by several decibels. Thus, IVsystems can require fewer base station antennas and/or achieve increased channel capacity through greater frequency reuse. With adaptive bearnforrning schemes the wireless system was seen to have greater margins and requires fewer antennas than with maximal ratio combining. Finally, we described how adaptive beamforming can be implemented in wireless radio with LMS, RLS and, DMI adaptive arrays. Thus, we have shown that adaptive beamforming is a practical means for increasing the channel capacity and performance of digital wireless systems. Keywords: Adaptive beamformer, Adaptive algorithms, Spatial-diversity combining techniques.