Comparative Study of Adaptive Beamforming Algorithms for Smart antenna Applications

Beamforming is signal processing techniques used to shape the antenna array pattern according to prescribed criteria. In this paper, a comparative study is presented for various adaptive antenna beamforming algorithms. Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS) and Sample Matrix Inversion (SMI) algorithms are studied and analyzed. we also consider some possible adaptive filters combinations, such as LMS with SMI weights initialization, and combined NLMS filters with a variable mixing parameter. These algorithms are simulated for a linear antenna array with different sizes, and results are discussed in terms of their Convergence speed, Max SLL and Null depths.