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机器学习驱动的启发MTM和寄生环优化,用于增强26 GHzMIMO天线阵列的隔离和增益

Linda Chouikhi¹, Chaker Essid¹, Bassem Ben Salah¹

¹SERCOM Laboratory, Tunisia Polytechnic School, University of Carthage, La Marsa P.O. Box 743-2078, Tunisia.

|October 29, 2025

概括

本研究介绍了一种人工智能驱动的框架,用于为5G设计高性能26GHz MIMO天线阵列. 机器学习模型优化天线元件,为先进的无线系统实现更好的增益和减少相互合.

科学领域:

电气工程电气工程
天线理论天线理论
超材料是指一种超材料.

背景情况:

5G毫米波 (mmWave) 通信需要高性能天线阵列.
为26 GHz设计紧,高效的MIMO天线带来了重大挑战.
超材料和人工智能为天线优化提供了潜在的解决方案.

研究的目的:

为5G开发26GHz MIMO天线阵列开发一个智能设计框架.
使用机器学习 (ML) 模型优化天线性能.
通过模拟和测量验证人工智能辅助设计方法.

主要方法:

一个紧的微条纹补丁天线被设计为26GHz的操作.
一个多层感知器 (MLP) 模型优化了元材料单元细胞尺寸以增强增益.
一个随机森林 (RF) 模型与贝叶斯优化调整了寄生环参数以最大限度地减少相互合.

主要成果:

单元补丁在26 GHz实现了S11 < -40 dB和宽带宽 (~26.2%).
通过MLP优化,天线收益增加了+2dB.
射频优化减少了26 GHz的相互合 (S12) 从-25 dB降至-58 dB.
模拟和测量结果与ML预测密切匹配,验证了设计框架.

结论:

人工智能辅助设计框架为开发下一代毫米波MIMO天线系统提供了快速可靠的方法.
拟议的智能设计方法有效优化5G应用的天线参数.
经过验证的框架加速了先进的无线通信技术的实现.

关键词:

26GHz的频率是26GHz的频率.5G是什么意思? 5G是什么意思?这是一个MIMO天线阵列.多层感知器多层感知器随机的森林随机的森林受到启发的超材料 (MTM)加强隔离,加强隔离.机器学习是机器学习.一个寄生虫的戒指.

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