Performance Analysis of Side Lobe Reduction for Smart Antenna Systems Using Genetic Algorithms (GA)

The growing demand for wireless communications necessitates greater radio spectrum utilisation in order to achieve better coverage, more capacity, and improved transmission quality. Several adaptive algorithms may be utilised to examine the adaptive beamforming technique of smart antennas, improving the performance of wireless communication networks. This article suggests an efficient and straightforward way for creating linear array antennas. The technique tries to lower the side lobe level (SLL) and redirect the primary beam in various directions. The use of genetic algorithms (GA) is also suggested as a fresh approach to adaptive beamforming. When used to optimise the excitation levels of the elements in a linear array based on fitness values, genetic algorithms are iterative stochastic optimizers that resemble the idea of the strongest surviving the weakest. In order to create a radiation pattern with the fewest side lobe levels, the excitation levels of the elements in the linear array are optimised using the adaptive evolutionary method. To aim the main beam in a particular direction, these algorithms calculate the proper side lobe levels and phase excitations for each antenna. In this paper, the side lobe level (SLL) is reduced using a genetic algorithm for a smart antenna system. The proposed work results were evaluated by MATLAB R2013a tool.