Antenna Array Beam Synthesis in Future Wireless Systems via Adaptive Crossover Newton-Raphson-Based Technique

Abstract

Beamforming has emerged as a key technology for beyond 5G and future 6G wireless communication systems, improving spectral efficiency, link dependability, and interference suppression. However, designing antenna array beam patterns remains a difficult multidimensional and nonlinear problem, with conventional techniques frequently failing to achieve rapid and stable convergence. To address this issue, this paper introduces the Adaptive Crossover Newton-Raphson-Based Optimizer (ACNRBO), which combines the Newton-Raphson method’s strong local search ability with an adaptive crossover mechanism to achieve the best balance of exploration and exploitation. The suggested approach is applied to Chebyshev and Shaped array beamforming designs, optimizing both amplitude excitation and phase parameters to reduce side lobe levels and enhance directivity. Extensive simulations compare ACNRBO to numerous cutting-edge algorithms, including standard NRBO, Black-winged Kite Algorithm (BKA), Evolutionary Mating Algorithm (EMA), Educational Competition Optimizer (ECO), and Four Vector Intelligent Metaheuristic (FVIM). The results show that ACNRBO achieves up to 60% lower objective function values, 50-70% less variation, and up to 45% faster convergence than competing approaches, all while providing smoother radiation patterns and higher beamforming precision. These results show ACNRBO’s efficacy and robustness as a potent optimization tool for next-generation adaptive antenna array systems.