Evaluation of Beamforming Optimization Techniques on Multibeam Antenna for 5G wireless Communications

With the rapid evolution of 5G wireless communications, the demand for efficient and reliable Multibeam Antenna (MBA) systems has increased significantly. Beamforming optimization techniques play a crucial role in maximizing the performance of these antenna systems. This paper presents an evaluation of three popular optimization algorithms, Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Spider Monkey Optimization (SMO), applied to beamforming optimization in an MBA for 5G wireless communications. The evaluation of these techniques is conducted based on several performance metrics, including scanning performance, scanning loss, gain, and radiation pattern. Each optimization algorithm is applied to the MBA system, and the resulting beamforming configurations are analyzed and compared. The experimental results indicate that while PSO and ABC demonstrate promising performance, SMO consistently outperforms the other two algorithms. SMO achieves higher scanning performance, lower scanning loss, improved gain, and more accurate radiation patterns compared to PSO and ABC. The superiority of SMO can be attributed to its unique optimization approach, which efficiently searches for optimal beamforming solutions in a high-dimensional space. The findings of this study provide valuable insights into the effectiveness of different optimization techniques for beamforming in MBA systems for 5G wireless communications. The results contribute to enhancing the performance and reliability of 5G wireless communications networks by enabling efficient MBA design and deployment.