Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks

Abstract

This paper presents a novel approach to synthesizing phased antenna arrays (PAAs) by combining Taylor-series expansion with neural networks (NNs), enhancing the PAA synthesis process for modern communication and radar systems. Synthesizing PAAs is crucial for these systems, offering versatile beamforming capabilities. Traditional methods often rely on complex analytical formulations or numerical optimizations, leading to suboptimal solutions or high computational costs. The proposed method uses Taylor-series expansion to derive analytical expressions for PAA radiation patterns and beamforming characteristics, simplifying the optimization process. Additionally, neural networks are employed to model the intricate relationships between PAA parameters and desired performance metrics, providing adaptive learning and real-time adjustments. A validation of the proposed method is performed on a dual-band 5G antenna, which exhibits marked resonances at 28.14 GHz and 37.88 GHz, with reflection coefficients of S11 = −19 dB and S11 = −19.33 dB, respectively. The integration of Taylor expansion with NNs offers improved efficiency, reduced computational complexity, and the ability to explore a broader design space. Simulation results and case studies demonstrate the effectiveness and applicability of the approach in practical scenarios. This work represents a significant advancement in PAA synthesis, showcasing the synergistic integration of mathematical modeling and artificial intelligence for optimized antenna design in modern communication and radar systems.