深度学习用于增强M-PSK和M-QAM波形信号的自动分类,专门用于单中继合作MIMO5G系统
Haithem Ben Chikha1, Alaa Alaerjan2, Randa Jabeur3
1Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia.
Scientific reports
|July 17, 2025
概括
一种新的深度学习方法准确地对5G系统中的M-PSK和M-QAM信号进行分类. 这种方法在合作网络中增强了自动调制分类 (AMC),即使有部分通道信息和低信号噪声比.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 自动调制分类 (AMC) 对软件定义无线电和可再生能源系统 (RES) 等自适应通信系统至关重要.
- 现有的方法在复杂的环境中面临挑战,其中包括部分通道状态信息 (CSI) 和相关通道.
研究的目的:
- 开发一种基于深度学习的新型AMC方法,用于识别5G合作MIMO系统中的M-PSK和M-QAM信号.
- 在部分CSI和空间相关通道条件下改善AMC性能.
主要方法:
- 使用一个卷积神经网络 (CNN) 分类器训练在歧视性特征 (高阶统计,差异非线性阶段峰值因子).
- 通过使用格拉姆-施密特直角化来减少特征维度,以提高训练效率.
- 采用集中决策策略,从多个天线汇总预测.
主要成果:
- 提出的基于CNN的AMC方法显著超过了基准机器学习分类器.
- 实现了卓越的分类准确性,精度,回忆和F测量.
- 在低信号噪声比 (SNR) 和各种调制顺序下证明有效.
结论:
- 新的CNN方法为在现实的5G合作场景中增强AMC提供了实际解决方案.
- 该方法显示出在先进的无线通信系统中应用的巨大潜力.
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