Analysis of adaptive algorithms for digital beamforming in Smart Antennas

Abstract

Smart Antennas have been gaining popularity in the recent times, as a means to enhance data rate. The reason behind this development is the availability of high-end processors to handle the complex computations involved. The major advantage of digital beamformer (smart antennas) is that phase shifting and array weighing can be performed on digital data rather than in hardware. This paper analyses the performance of three adaptive algorithms — Least Mean Square (LMS), Recursive Least Square (RLS) and Conjugate Gradient Method (CGM) for computing the array weights. In this paper digital beamforming is also performed using kalman based normalised Least Mean Square algorithm. This adaptive algorithm promises very high rate of convergence, highly reduced mean square error and low computational complexity compared to the existing adaptive algorithms. The weights obtained by the above algorithm are then used to steer the antenna array beam in the direction of interest, thereby enhancing SNR. One of the major requirements for Long Term Evolution (LTE), high datarate, can hence be achieved by smart antennas.