基于机器学习的技术,用于对UMTS LTE频段准Yagi天线的共振和方向性预测
Md Ashraful Haque1,2, Dipon Saha1, Samir Salem Al-Bawri3,4
1Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia.
Heliyon
|October 9, 2023
概括
本研究部署了机器学习,以提高LTE天线性能,用于2100 MHz的UMTS应用. 优化的准-Yagi-Uda天线显示出卓越的效率和收益,通过模拟和ML预测进行验证.
科学领域:
- 电气工程 电气工程
- 天线理论天线理论
- 机器学习应用 机器学习应用
背景情况:
- LTE (长期进化) 应用需要高性能天线来实现可靠的无线通信.
- 准-Yagi-Uda天线提供适合特定频段的定向特性,例如2100 MHz UMTS频段.
- 机器学习 (ML) 提供了优化天线设计和预测性能特征的机会.
研究的目的:
- 部署一种机器学习技术,以提高LTE应用的性能.
- 为2100 MHz UMTS频段设计和评估一个紧的准-Yagi-Uda天线.
- 使用ML来准确预测天线频率和定向性.
主要方法:
- 使用CST (计算机模拟技术) 进行天线设计和模拟.
- 用于天线表征的RLC等效电路建模.
- 监督回归机器学习模型 (线性回归,回归) 用于性能预测.
- 用于验证的实验测量.
主要成果:
- 拟议的准Yagi-Uda天线实现了-38.40dB的反射系数,357MHz的带宽和6.9dB的最大增益.
- 在低成本的FR4基板上,天线具有很高的定向性 (7.67) 和效率 (89.9%).
- 线性回归和回归模型在预测天线方向性和频率方面表现出高准确性.
结论:
- 设计的准Yagi-Uda天线是UMTS LTE应用程序的可行候选者.
- 机器学习技术有效地提高天线性能预测和设计.
- 模拟,测量和ML的整合为天线开发提供了一个强大的方法.
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