Differential Evolution-Based End-Fire Realized Gain Optimization of Active and Parasitic Arrays

TL;DR

This paper introduces a differential evolution-based optimization method for designing active and parasitic antenna arrays that achieve high realized gain with simplified structures and reduced computational resources.

Contribution

The paper presents a novel multi-objective optimization approach using differential evolution for end-fire array gain enhancement, achieving state-of-the-art results with simpler designs.

Findings

Achieved high realized gain comparable to or exceeding existing designs.

Significant reduction in optimization time compared to full-wave simulations.

Demonstrated robustness of the optimized arrays under parameter deviations.

Abstract

We propose a novel approach for boosting the realized gain in enhanced directivity arrays with both active and parasitic dipoles as radiating elements. The optimization process involves two main objectives: maximizing the end-fire gain and minimizing the reflection coefficient to ensure high realized gain. In the first step, the current excitation vector of the fully driven array is selected to maximize the end-fire gain. Then, all but one of the dipoles are reactively loaded according to their input impedance. Following that, the optimization focuses on the inter-element distance, computing the one that offers a favorable balance between the gain and the total efficiency. This multi-objective optimization leverages the differential evolution (DE) algorithm and utilizes a simple wire dipole as the unit element. Full-wave simulations further confirm the accuracy of our theoretical results. Our two- and three-element parasitic arrays achieve realized gain comparable to state-of-the-art designs, without relying on intricate unit elements or resource-intensive simulations. Moreover, our four- and five-element parasitic arrays deliver the highest realized gain values reported in the literature. The simplicity of our approach is validated by significant time savings, with theoretical models completing optimizations much faster than full-wave simulations. Additionally, a sensitivity analysis confirms the robustness of the proposed optimization algorithm, demonstrating that the optimized design parameters remain effective even under small deviations in loads and element positions. Finally, the proposed parasitic arrays are well-suited for base station antennas due to their compact design, reduced power consumption, and simplified hardware requirements, making them ideal for modern communication systems.