A Performance Analysis of CLMS and Augmented CLMS Algorithms for Smart Antennas

An adaptive beam former is a device, which is able to steer and modify an array's beam pattern in order to enhance the reception of a desired signal, while simultaneously suppressing interfering signals through complex weight selection. However, the weight selection is a critical task to get the low Side Lobe Level (SLL) and Low Beam Width. It needs to have a low SLL and low beam width to reduce the antenna's radiation/reception ability in unintended directions. The weights can be chosen to minimize the SLL and to place nulls at certain angles. A vast number of possible window functions that are available to provide the weights to be used in Smart Antennas. This paper presents various traditional windowing techniques such as Binomial, Kaiser-Bessel, Blackman, Gaussian, and so on for computing weights for adaptive beam forming and also neural based methods like, Least Mean Square (LMS), Complex LMS (CLMS) [5], and Augmented CLMS (ACLMS) [1] algorithms. This paper discusses about various observations on signal processing techniques of Smart Antennas, that compromise between SLL and beam width (Directivity), to improve the base station capacity in Cellular and Mobile Communications and also the performance analysis of CLMS and ACLMS in terms of SLL and beam width, error convergence rate.