A comparison between different adaptive beamforming techniques

Smart antennas consist of an antenna array and an adaptive processor which is used to optimise some specific criteria. In this paper, different adaptive beamforming algorithms like Least Mean Square (LMS), Recursive Least Square (RLS), Sample Matrix Inversion (SMI), Conjugate Gradient (CG) and Constant Modulus (CM) algorithms are looked into. LMS, RLS and CG beamformers iteratively converge to optimum weights for signal and interference conditions. Here number of operations per iteration and number of iterations needed for convergence contribute to computational complexity. In case of SMI beamformer the dimension of block of received data snapshots contributes to complexity. The performance of these beamformers is analysed in terms of convergence speed and complexity. Simulations have been carried out with 8, 16 and 32-element array. The complexity increases with increase in number of array elements whereas their beam width reduces. Reduction in beamwidth improves angular resolution in beam steering. It is observed that the LMS beamformer is a potential candidate in terms of performance trade off between array dimensions and computational complexity. Results are consistent for beam steering towards a specific signal and interference direction as well as for varying arrival angles of signal and interference.