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<p>This paper presents the design and machine learning-based optimization of a compact single-port MIMO antenna operating at 37&#xa0;GHz for millimeter-wave 5G applications. The antenna is fabricated on a Rogers RT5880 substrate and optimized using a neural network trained on 670 CST simulation samples to enhance gain, bandwidth, and impedance matching. The data-driven model efficiently explores the design space, reducing the need for time-consuming full-wave simulations. The optimized single antenna achieves a compact size of 4.21 × 7&#xa0;mm², bandwidth of 0.9&#xa0;GHz (36.9–37.8&#xa0;GHz), return loss better than − 30 dB, and radiation efficiency exceeding 83%. A 1 × 4 antenna array configuration based on the optimized element further improves the peak gain to 13.2 dB. Experimental validation confirms close agreement with simulated results, highlighting the reliability of the ML-driven design approach. The proposed methodology demonstrates a scalable and efficient path to antenna miniaturization and performance enhancement for 5G hardware. These results position the design as a strong candidate for integration into compact mobile and IoT devices operating in the mmWave band.</p>

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MIMO antenna optimization for millimeter-wave 5G applications using machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月20日

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  • Masuod Ghasemi

摘要

This paper presents the design and machine learning-based optimization of a compact single-port MIMO antenna operating at 37 GHz for millimeter-wave 5G applications. The antenna is fabricated on a Rogers RT5880 substrate and optimized using a neural network trained on 670 CST simulation samples to enhance gain, bandwidth, and impedance matching. The data-driven model efficiently explores the design space, reducing the need for time-consuming full-wave simulations. The optimized single antenna achieves a compact size of 4.21 × 7 mm², bandwidth of 0.9 GHz (36.9–37.8 GHz), return loss better than − 30 dB, and radiation efficiency exceeding 83%. A 1 × 4 antenna array configuration based on the optimized element further improves the peak gain to 13.2 dB. Experimental validation confirms close agreement with simulated results, highlighting the reliability of the ML-driven design approach. The proposed methodology demonstrates a scalable and efficient path to antenna miniaturization and performance enhancement for 5G hardware. These results position the design as a strong candidate for integration into compact mobile and IoT devices operating in the mmWave band.