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<p>The geometric configuration of monopole patch antenna (MPA) structures exerts a substantial influence on key performance parameters, including impedance matching, radiation efficiency, radiation pattern, gain, and directivity. Consequently, the optimization of patch geometry is a pivotal step in enhancing the overall performance of MPAs. In this study, the design parameters of the MPA were optimized using the Red Fox Optimization (RFO) algorithm, a recently developed metaheuristic technique. The RFO algorithm was employed to minimize return loss (S₁₁) and improve impedance matching characteristics. The optimization procedure was conducted using an integrated framework that combined MATLAB and CST Studio Suite. A comparative analysis was carried out to evaluate the performance of various optimization algorithms in achieving superior impedance matching. The results demonstrated that the RFO algorithm outperformed conventional optimization methods, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). Furthermore, several design strategies were implemented to achieve wideband radiation characteristics. Accurate evaluation of the fitness function contributed to reducing undesired radiation and enhancing the overall efficiency of the optimization process. As a result, a compact, cost-effective, dual-band MPA was successfully designed, fabricated, and experimentally validated. The proposed antenna exhibits wideband functionality and supports high data rate transmission, effectively covering the n79 (4400–5000&#xa0;MHz) and n46 (5150–5925&#xa0;MHz) frequency bands within the sub-6&#xa0;GHz spectrum for 5G New Radio (NR) applications.</p>

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Optimization of Compact, Dual-Band Monopole Patch Antenna for 5G (n79/n46) Applications Using the Red Fox Optimization (RFO) Algorithm

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Semih Pak,
  • Muhammet Tahir Güneser

摘要

The geometric configuration of monopole patch antenna (MPA) structures exerts a substantial influence on key performance parameters, including impedance matching, radiation efficiency, radiation pattern, gain, and directivity. Consequently, the optimization of patch geometry is a pivotal step in enhancing the overall performance of MPAs. In this study, the design parameters of the MPA were optimized using the Red Fox Optimization (RFO) algorithm, a recently developed metaheuristic technique. The RFO algorithm was employed to minimize return loss (S₁₁) and improve impedance matching characteristics. The optimization procedure was conducted using an integrated framework that combined MATLAB and CST Studio Suite. A comparative analysis was carried out to evaluate the performance of various optimization algorithms in achieving superior impedance matching. The results demonstrated that the RFO algorithm outperformed conventional optimization methods, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). Furthermore, several design strategies were implemented to achieve wideband radiation characteristics. Accurate evaluation of the fitness function contributed to reducing undesired radiation and enhancing the overall efficiency of the optimization process. As a result, a compact, cost-effective, dual-band MPA was successfully designed, fabricated, and experimentally validated. The proposed antenna exhibits wideband functionality and supports high data rate transmission, effectively covering the n79 (4400–5000 MHz) and n46 (5150–5925 MHz) frequency bands within the sub-6 GHz spectrum for 5G New Radio (NR) applications.