基于回归机器学习的高效双带MIMO天线设计,用于毫米波5G应用和增益预测
Redwan A Ananta1, Md Ashraful Haque1, Geamel Alyami2
1Department of Electrical and Electronic Engineering, Daffodil International University, Dhaka, Bangladesh.
Scientific reports
|August 6, 2025
概括
本研究介绍了一种适用于5G毫米波系统的紧型双频微带补丁天线. 机器学习准确地预测了天线性能,为未来的无线网络提供了高效的设计.
科学领域:
- 电气工程 电气工程
- 电磁学 电磁学 电磁学 电磁学
- 无线通信无线通信
背景情况:
- 由于无线通信系统的发展,对毫米波 (mm-Wave) 频率的高性能天线的需求正在迅速增加.
- 紧而高效的天线设计对于5G及其他应用至关重要.
研究的目的:
- 设计和分析一个在双频 (28GHz和38GHz) 运行的微条纹补丁天线系统.
- 为了评估天线在单元,2元阵列和4端口MIMO配置中的性能.
- 开发和验证用于预测天线性能的机器学习框架.
主要方法:
- 在罗杰斯RT5880基板上的微条纹补丁天线的设计,具有紧的足迹 (31.26mm × 31.26mm).
- 性能分析包括增益,带宽,隔离,信封相关系数 (ECC),多样性增益 (DG) 和辐射效率.
- 使用五种回归模型 (额外树回归评估) 实现基于机器学习的预测框架.
主要成果:
- 该天线系统实现了高收益 (9 dB和8.4 dB) 和宽带宽 (2.55 GHz和5.77 GHz).
- 获得了优异的隔离 (>35 dB和>29 dB),非常低的ECC (<0.0001),高的DG (>0.999) 和辐射效率 (>98%和>99%).
- 额外树木回归模型在预测天线性能方面显示出高准确性,MAE,MSE和RMSE的错误率较低,隔离预测准确率约为93%.
结论:
- 拟议的紧型双频天线系统在毫米波应用中表现出卓越的性能.
- 机器学习框架为准确的天线性能预测提供了有效的工具.
- 该天线系统非常适合未来的高容量无线通信网络.
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