具有回归机器学习的高性能网格结构的MIMO天线,用于高速亚THz和THz6G物联网应用
Jamal Hossain Nirob1, Isha Das2, Kamal Hossain Nahin1
1Department of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1341, Bangladesh.
Scientific reports
|August 26, 2025
概括
这项研究优化了使用机器学习进行下一代无线通信的特拉赫兹 (THz) 天线. 这种新的设计实现了高收益和效率,为先进的物联网 (IoT) 和6G应用铺平了道路.
科学领域:
- 电气工程
- 天线设计
- 机器学习应用
背景情况:
- 下一代无线系统需要能够在特拉赫兹 (THz) 频谱中运行的天线.
- 优化天线增益和隔离对于提高无线通信和物联网 (IoT) 系统的性能至关重要.
- 机器学习为改进天线特征的设计和预测提供了潜力.
研究的目的:
- 引入一种创新的机器学习方法来优化THz频谱中的天线增益.
- 设计和分析一个基于聚胺的紧型天线,用于THz通信.
- 使用模拟和电路建模验证天线模型,并使用回归算法预测MIMO天线增益.
主要方法:
- 使用CST-2018模拟和RLC电路建模进行天线设计和分析.
- 通过通过ADS构建的类似RLC等效模型进行验证.
- 应用监督回归机器学习算法 (包括XGB回归) 来预测MIMO天线增益.
主要成果:
- 拟议的天线在两个操作频段实现了11.91dB和12.21dB的高峰收益.
- 显示出异常的隔离度 (31.43dB,36.1dB) 和高辐射效率 (92.42%,86.93%).
- 在预测MIMO天线增益方面,XGB回归实现了超过96%的可靠性,验证了机器学习方法.
结论:
- 设计的THz天线表现出卓越的性能指标,使其适用于高速6G应用.
- 机器学习模型的整合提高了MIMO天线系统的设计效率和容量预测.
- 这项研究为在THz频谱中运行的下一代无线通信系统提供了新的解决方案.
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