基于神经网络的毫米波5G无线网络的最佳算法
Liang Chen1, Shebnam M Sefat2,3, Ki-Il Kim4
1Jilin Provincial Institute of Education, Chang Chun 130022, China.
Heliyon
|July 7, 2023
概括
第五代 (5G) 网络使用毫米波 (mmWave) 频谱来实现更快的速度. 一个新的混合智能反射表面和深度神经网络改善了毫米波5G通信的通道估计.
科学领域:
- 无线通信工程 无线通信工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 第五代 (5G) 无线网络使用毫米波 (mmWave) 频谱来提高吞吐量.
- 毫米波通信的挑战包括传播不良和复杂的协调,阻碍性能.
- 智能反射面引入了复杂性和不精确性来引导状态信息.
研究的目的:
- 为了应对毫米波5G通信中的通道估计挑战.
- 提出一种新的混合智能反射表面解决方案.
- 开发一个改进的深度神经网络 (DNN) 进行有效的通道估计.
主要方法:
- 设计了一个由被动元件和射频电路组成的混合智能反射表面.
- 开发了一种基于深度神经网络 (DNN) 的改进技术,用于通道估计.
- 通过模拟来评估性能.
主要成果:
- 提出的基于DNN的技术证明了优越的通道估计性能.
- 混合智能反射表面有效地管理了通道复杂性.
- 模拟结果显示,毫米波5G服务质量有所改善.
结论:
- 混合智能反射表面和DNN方法有效地克服了毫米波频道估计挑战.
- 该解决方案增强了毫米波5G在高性能无线连接方面的潜力.
- 拟议的技术为实现5G毫米波的全部能力提供了一个有希望的途径.
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