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机器学习优化的紧型可穿戴频率可重新配置天线,用于6GHz以下/毫米波5G集成

Abubakar Salisu¹, Mahmud Abd Elwanis², Issa Elfergani^2,3

¹Department of Biomedical and Electronics Engineering, University of Bradford, Bradford, UK. a.salisu@bradford.ac.uk.

Scientific reports

|December 29, 2025

概括

这项研究介绍了一种紧的可穿戴天线,该天线通过机器学习优化,用于5G集成. 它实现了3.5GHz和28GHz的双频段操作,确保稳定的性能并满足移动设备的安全标准.

科学领域:

电磁学和波浪传播
无线通信系统无线通信系统
机器学习在工程中的应用.

背景情况:

由于频率的巨大差异,未来的5G无线系统在6GHz以下和毫米波 (mmWave) 频段之间面临整合挑战.
现有的天线解决方案往往难以在这些不同的频率范围内为可穿戴应用提供高效和可重新配置的操作.

研究的目的:

提出和验证一个机器学习优化的,紧的,可穿戴的,频率可重新配置的天线,用于无的6GHz/mmWave 5G集成.
为了证明双频段 (3.5GHz和28GHz) 运行具有高性能指标和符合安全法规.

主要方法:

在柔性基板上制造一个可重新配置的天线,最初在28GHz发出共振,并为双频段功能提供H形插槽.
集成PIN二极管用于在ON (3.5GHz和28GHz) 和OFF (28GHz) 状态之间进行频率重新配置.
利用监督机器学习回归框架,特别是决策树算法,来预测天线性能 (S11).

主要成果:

在3.5GHz和28GHz实现了双频段运行,在ON状态下具有显著的带宽 (25.4%,73.2%),增益 (3.63dBi,5.25dBi) 和高辐射效率 (90.5%,88%).
在OFF状态下,天线在28 GHz保持了72.9%的带宽和6.2dBi的增益,效率为89%.
决策树模型在预测S11时表现出最先进的准确性 (R2: 97.80%),误差指标最小.

结论:

拟议的频率可重新配置天线有效地集成5G应用的6GHz以下和毫米波波段,提供稳定的车身性能.
机器学习优化显著提高了天线参数的预测准确性,简化了设计过程.
该天线符合FCC和ICNIRP特定吸收率 (SAR) 的安全标准,使其适合未来的5G可穿戴设备.

关键词:

曲调查调查曲调查频率的重新配置性频率的重新配置性机器学习是机器学习.一个PIN二极管这就是为什么SAR SAR SAR.

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