Abstract:
Adaptive beamforming using smart antennas is one of the potential solution to the demand for increased capacity in wireless communication networks. The Least Mean Square (LMS) algorithm has been identified as a suitable technique that optimises the Signal to Noise Ratio (SNR) of the desired signal in a particular direction. However, although it gives an optimum solution, the LMS exhibits slow convergence rate. This thesis proposes the development and the analysis of the Normalized Least Mean Square (NLMS) algorithm as a method to improve the convergence rate of the standard LMS algorithm thus, increasing channel capacity and spectrum efficiency. The proposed adaptive beamforming scheme uses an array of antennas to realise maximum reception in a specified direction. This is achieved by adjusting the weights of each of the antennas with changing signal environment. The NLMS algorithm is an extension of LMS algorithm where the step size parameter is chosen based on current input values. It shows greater stability with unknown signals. The theoretical formulation results show an insignificant increase in the computational complexity of the NLMS algorithm. The simulation results for both the NLMS and the standard LMS agree closely. However, it is established that the proposed method has a better convergence rate of the Mean Square Error (MSE) and updates the weights in less number of iterations in time precisely 12 iterations. The performance of the NLMS algorithm is compared to that of the block LMS algorithm and Recursive Least Squares. The proposed algorithm performed better in terms of beam steering (narrower beam at the desired angle 30°), and in terms of null deep capability-42dB as compared to the block LMS algorithm -30dB and the RLS algorithm -41.94dB.
