用于下一代28/38 GHz的5G无线网络的高隔离双频MIMO天线,具有基于机器学习的增益预测
Md Ashraful Haque1, Redwan A Ananta1, Md Sharif Ahammed1
1Department of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1207, Bangladesh.
Scientific reports
|July 1, 2025
概括
这项研究使用机器学习 (ML) 来增强5G毫米波应用,以提高吞吐量. 一个随机森林回归模型准确地预测天线增益,优化高频段5G服务的性能.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 电信 电信服务 电信服务 电信服务
背景情况:
- 5G毫米波应用需要高吞吐量和高效的天线设计.
- 传统的天线设计在优化高频频段的性能方面面临着挑战.
- 机器学习 (ML) 提供了改善天线性能预测和优化的潜力.
研究的目的:
- 实施ML方法,以提高5G毫米波应用的吞吐量.
- 为5G应用设计和评估使用罗杰斯RT 5880材料的紧型微条纹补丁天线 (MPA).
- 使用监督回归ML模型进行精确的天线增益预测.
主要方法:
- 使用高级设计系统 (ADS) 和CST进行天线设计和模拟.
- 集成用于天线分析的RLC等效电路模型.
- 应用和比较五个监督回归ML模型,包括随机森林回归 (RFR).
主要成果:
- 设计的MPA的最大增益为10.14dB,隔离>29dB,带宽宽 (27.232.2GHz和36.540.7GHz).
- 随机森林回归 (RFR) 模型在预测天线增益方面表现出卓越的准确性和最低的误差.
- 在评估天线适合5G应用时,ML方法被证明有效.
结论:
- 拟议的基于ML的方法,特别是RFR,显著提高了5G应用中天线增益的预测准确性.
- 开发的紧型MPA设计符合毫米波5G系统的关键性能指标.
- 这项研究验证了ML技术与天线设计的整合,以优化5G通信系统.
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