基于机器学习的设计和优化,一个紧的双端口CPW为无线通信提供UWB MIMO天线
Jayant Kumar Rai1,2, Swati Yadav3, Ajay Kumar Dwivedi4
1Department of Electronics and Telecommunication, RKR Government Polytechnic, Janjgir Champa, Chhattisgarh, India.
Scientific reports
|April 22, 2025
概括
使用机器学习 (ML) 优化的一种新型紧型双端口天线,实现了用于下一代无线通信的8.7 GHz带宽,包括5G和Wi-Fi. 决策树ML算法提供了99.92%的准确性.
科学领域:
- 电气工程 电气工程
- 天线设计天线设计
- 无线通信无线通信
背景情况:
- 下一代无线系统需要高性能天线,能够支持超宽带宽.
- 多输入多输出 (MIMO) 技术对于提高无线通信可靠性和数据速率至关重要.
- 机器学习 (ML) 为复杂的天线设计提供了强大的优化功能.
研究的目的:
- 为了呈现一个紧的双端口共平面波导 (CPW) 输入超宽带 (UWB) 天线.
- 使用各种机器学习 (ML) 算法优化天线设计.
- 为了验证天线的性能,用于下一代无线通信标准.
主要方法:
- 在FR4环氧基板上设计和模拟CPW供应的UWB MIMO天线.
- 使用人工神经网络 (ANN),极端梯度增强 (XGBoost),随机森林 (RF),K-最近邻居 (KNN) 和决策树 (DT) ML算法优化天线参数.
- 拟议天线的制造和测量,以验证模拟结果.
主要成果:
- 实现了8.7 GHz (2.78-11.48 GHz) 的超宽带宽.
- 决策树ML算法在优化方面表现出最高的准确性 (99.92%).
- 对S参数,辐射模式和MIMO参数 (DG,CCL,MEG,ECC,TARC) 的模拟和测量数据之间有很好的相关性.
结论:
- 拟议的UWB MIMO天线适用于各种下一代无线应用,包括5G和Wi-Fi.
- 基于ML的优化显著提高了天线设计效率和性能.
- 经过验证的天线设计为未来的无线通信系统提供了有希望的解决方案.
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