Particle swarm optimization for beamforming design in a cognitive radio

Abstract

Beamforming is essential for improving transmission in wireless sensor networks (WSNs), particularly in cognitive radio networks (CRNs) with several secondary users (SU) equipped with transmitting antennas. Optimizing beamforming while minimizing interference with primary users (PU) is of great interest. This study proposes an improved particle swarm optimization (PSO) algorithm to enhance beamforming performance. This approach aims to maximize the power of the beam directed to the SU receiver while controlling interference in the PU protection region. The results show that this algorithm constantly improves beam focus and signal-to-noise ratio to effectively optimize beamforming. Firstly, beam focusing becomes narrower as the number of antenna elements increases, generating optimal transmission conditions. Secondly, the algorithm achieves a considerable improvement in signal-to-noise ratio as the number of antenna elements increases. Furthermore, optimization performance improves as the number of antenna elements increases, as shown by the best fitness values. The simulations also illustrate the performance of the proposed method.