A Comprehensive Review on Frequency Reconfigurable Antennas for 4G, 5G and sub-6 GHz Systems with Emerging Techniques
DOI:
https://doi.org/10.37256/jeee.5120269368Keywords:
Frequency Reconfigurable Antenna (FRA), Artificial Intelligence (AI), Machine Learning (ML), flexible antennas, Positive-Intrinsic-Negative (PIN) diodes, varactors, metamaterialsAbstract
The rapid evolution of wireless communication systems across sub-6 GHz and millimeter Wave bands has intensified the demand for Frequency Reconfigurable Antennas (FRAs) capable of adaptive, multi-standard operation. This review presents a comprehensive performance-driven synthesis of reconfigurable patch antenna architectures developed from 1963 to 2026, with emphasis on recent advances after 2020. Reconfiguration techniques are systematically classified into electrical, mechanical, and metamaterial-based approaches and evaluated based on key performance parameters including tuning range, radiation efficiency, switching speed, biasing complexity, and structural footprint. Electrical methods employing PIN diodes, varactors, and RF-MEMS switches are analyzed alongside mechanical actuators, liquid-metal and phase-change materials, magneto-dielectric substrates, and metasurface-based mechanisms to establish critical trade-offs between actuation latency, power handling, and integration feasibility. A unified benchmarking framework incorporating comparative performance mapping and response-time frontiers is introduced to support informed design selection. Additionally, the emerging role of machine learning in reducing simulation overhead and enabling predictive optimization is discussed as a transformative paradigm in antenna engineering. The review concludes by outlining research challenges and hybrid strategies essential for 5G and future 6G adaptive communication systems.
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Copyright (c) 2026 A. Y. Deshpande, et al.

This work is licensed under a Creative Commons Attribution 4.0 International License.
