Hybrid Adaptive Beamforming Algorithms for Smart Antennas

The adoption of smart antenna system is a promise to the solutions of the wireless communication impairments like inefficient utilization of frequency spectrum, signal fading due to multipath propagation, etc. The smart antenna works in conjunction with digital signal processor which is responsible to adjust various parameters of the system in order to phase out interference signals and to enhance reception in the desired direction(s). In this paper, an attempt is made to develop various adaptive beamforming algorithms that lead to overall improvement in the performance of the smart antennas. Three complex adaptive beamforming algorithms like Complex Least Mean Squares (CLMS) algorithm, Augmented Complex Least Mean Squares (ACLMS) algorithm, and Adaptive Nonlinear Gradient Descent (ANGD) algorithms are considered for beamforming in smart antennas. Characteristics like Half Power Beam Width (HPBW), Side Lobe Level (SLL) and Mean Square Error (MSE) convergence rate and Tracking the desired signal are considered for the evaluation of performance of the smart array. Three new hybrid algorithms are proposed using the convex hybridization. The hybrid algorithm is formed by the convex combination of any two of the three algorithms in pursuit of performance improvement. The performance of these hybrid algorithms with respect to the important array characteristics is evaluated. It is identified that each of the three hybrids is superior to its individual filters.