Advanced Helical Antenna Design for X-Band Applications Using AI
Mohammed Yousif Zeain,
Maisarah Abu,
Apriana Toding,
Zahriladha Zakaria,
Hussein Alsariera,
Ihsan Ullah,
Ali Abdulateef Abdulbari,
Hamizan Yon,
Bilal Salman Taha and
Muhammad Inam Abbasi
This paper presents the design, fabrication, and characterization of a novel 3D-printed helical antenna operating within the 9.4-10.8 GHz frequency band. The antenna, employing a lightweight paper substrate and a strip-based helical structure, exhibits robust circular polarization characteristics and wideband operation. Rigorous simulations predict a peak CP gain of 11.7 dBi at 9.8 GHz and a high simulated radiation efficiency of 95%. Experimental measurements validate these predictions, achieving a peak CP gain of 11.6 dBi at 9.8 GHz. This research demonstrates the potential of 3D-printed helical antennas for diverse applications in modern wireless communication systems, including 5G, satellite communication, and radar. Furthermore, this study leverages the power of Artificial Intelligence (AI) by employing the Grey Wolf Optimizer (GWO), a sophisticated metaheuristic algorithm, to optimize the antenna's design. The GWO algorithm is utilized to efficiently search the design space and identify optimal values for key parameters, such as the number of turns, helix pitch, and helix diameter, with the objective of maximizing antenna gain to achieve a target of 15 dBi. This research highlights the potential of AI-driven optimization techniques in advancing the design of high-performance antennas for emerging wireless communication systems.