A Study of Adaptive Beamforming Techniques Using Smart Antenna for Mobile Communication

Mobile radio network with cellular structure demand high spectral efficiency for minimizing number of connections in a given bandwidth. One of the promising technologies is the use of “Smart Antenna”. A smart antenna is actually combination of an array of individual antenna elements and dedicated signal processing algorithm. Such system can distinguish signal combinations arriving from different directions and subsequently increase the received power from the desired user. Wireless systems that enable higher data rates and higher capacities have become the need of hour. Smart antenna technology offer significantly improved solution to reduce interference level and improve system capacity. With this technology, each user’s signal is transmitted and received by the base station only in the direction of that particular user. Smart antenna technology attempts to address this problem via advanced signal processing technology called beam-forming. The advent of powerful low-cost digital signal processors (DSPs), generalpurpose processors (and ASICs), as well as innovative software-based signal-processing techniques (algorithms) have made intelligent antennas practical for cellular communications systems and makes it a promising new technology. Through adaptive beam-forming, a base station can form narrower beam toward user and nulls toward interfering users. In this thesis, both the block adaptive and sample-by-sample methods are used to update weights of the smart antenna. Block adaptive beam-former employs a block of data to estimate the optimum weight vector and is known as sample matrix inversion (SMI) algorithm. The sample-by-sample method updates the weight vector with each sample. Various sample-bysample methods, attempted in the present study are least mean square (LMS) algorithm, constant modulus algorithm (CMA), least square constant modulus algorithm(LS-CMA) and recursive least square (RLS) algorithm. In the presence of two interfering signals and noise, both amplitude and phase comparison between desired signal and estimated output, beam patterns of the smart antennas and learning characteristics of the above mentioned algorithms are compared and analyzed. The recursive least square algorithm has the faster convergence rate; however this improvement is achieved at the expense of increase in computational complexity. Smart antennas technology suggested in this present work offers a significantly improved solution to reduce interference levels and improve the system capacity. With this novel technology, each user’s signal is transmitted and received by the base station only in the direction of that particular user. This drastically reduces the overall interference in the system. Further through adaptive beam forming, the base station can form narrower beams towards the