基于机器学习的技术,用于中频段5G Yagi天线的增益和共振预测
Md Ashraful Haque1,2, Md Afzalur Rahman3, Samir Salem Al-Bawri4,5
1Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak, Malaysia.
Scientific reports
|August 3, 2023
概括
机器学习技术在5G n78频段应用中显著提高了准-Yagi-Uda天线的性能. 这项研究表明,ML模型准确地预测天线频率和增益,达到接近99%的准确性.
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:
- 5G通信系统需要高性能天线在特定频段工作,如n78.8.
- 传统的天线设计和优化可能是复杂和耗时的.
- 机器学习为改进天线设计和性能预测提供了潜力.
研究的目的:
- 为了研究机器学习 (ML) 技术的应用,以提高准Yagi-Uda天线的性能.
- 开发一个准确的ML模型来预测天线共振频率和收益.
- 为了评估优化天线对5G n78频段应用的适用性.
主要方法:
- 利用CST建模工具设计和模拟5G n78频段的Yagi-Uda天线.
- 使用模拟和电路设计软件推导出天线相当的RLC电路模型.
- 应用监督回归机器学习模型来预测天线频率和增益.
主要成果:
- 设计的Yagi-Uda天线具有3.5GHz的工作频率,520MHz带宽,6.57dB增益和97%的效率.
- 线性回归和高斯过程回归模型在预测共振频率和增益方面实现了高精度 (R平方~99%).
- 经过评估的其他ML技术相比,ML模型表现出更高的性能.
结论:
- 机器学习为准确预测5G天线性能提供了一种有效的方法.
- 开发的准-Yagi-Uda天线是n78频段中5G通信系统的合适候选者.
- 这种方法可以加速天线设计和优化流程,用于未来的无线应用.
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