Planar Antenna Arrays Beamforming Using Various Optimization Algorithms

Abstract

Due to the importance of beamforming in improving the communication systems performance, this paper presents a novel study of beamforming of planar antenna arrays (PAAs) utilizing the Improved Grey Wolf Optimization (I-GWO) algorithm with the goal of minimizing the peak sidelobe level (PSLL). It is very important to suppress the sidelobe level (SLL) because it minimizes interference and received noise. A two-dimensional (2D) optimization method is presented to find the optimal amplitude excitations and element placements in PAA. The effectiveness of beamforming optimization using the I-GWO is illustrated by comparing it with different metaheuristic algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Hybrid Particle Swarm Optimization with Gravitational Search Algorithm (PSOGSA), Runge Kutta Optimizer (RUN), Slime Mould Algorithm (SMA), Harris Hawks Optimization (HHO), as well as the original Grey Wolf Optimizer (GWO). Simulation findings show that antenna array beamforming using I-GWO is effective using the 2D optimization method compared to the other algorithms, where the 2D technique achieved the most decreased SLL with the fewest array elements, which helps reduce the cost of the entire system. This clearly shows that I-GWO is very efficient and can be applied to solve different beamforming optimization problems. It can also be used for the radiation pattern synthesis of other antenna array geometries for different wireless networks applications.

Keywords

• Beamforming
• grey wolf optimizer
• optimization algorithms
• planar antenna arrays
• sidelobe level minimization
• smart antennas