深度残余网络增强了多层残余的残余网络,用于5G和超越系统的无线电信号自动分类
Randa Jabeur1, Alaa Alaerjan2, Haithem Ben Chikha3
1Department of Computer Science,College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia.
Scientific reports
|February 2, 2026
概括
一个新的深度残余网络与多层残余的残余连接 (DRN+RoR) 改进了5G波形的自动调制分类. 这种先进的方法在复杂的无线环境中提高了准确性和稳定性.
科学领域:
- 无线通信无线通信
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 自动调制分类 (AMC) 对5G和超越网络的智能接收器至关重要.
- 具有先进调制 (16/64-QAM) 的多种多载波波形 (OFDM,FOFDM,FBMC,UFMC,WOLA) 构成了分类挑战.
研究的目的:
- 建议使用深度剩余网络 (DRN) 增强多层剩余的剩余 (RoR) 连接 (DRN+RoR) 的新型AMC框架.
- 在现实的通道条件下,对各种5G候选波形进行高级调制格式的分类.
主要方法:
- 实现一个DRN架构与多层次的RoR连接,用于深层次的层次特征提取.
- 顺序浮动向前选择 (SFFS) 的应用,以优化特征和减少维度.
- 在时间和频率选择性色和多种5G通道模型 (TDL-A,TDL-B,TDL-C) 下进行了广泛的模拟.
主要成果:
- 与最先进的基线相比,拟议的DRN+RoR方法显示出更高的分类准确度,回忆,精度和F1得分.
- 在各种信号噪音比率 (SNR) 和波形类型中实现更高的稳定性和准确性.
- 在各种5G通道条件下保持强大的概括性和强大的分类准确性.
结论:
- DRN+RoR是一个有效和可扩展的解决方案,用于在5G和超越系统中的强大的调制识别.
- 该框架为动态通信环境的自适应信号处理提供了显著的改进.
- 能够实现更可靠的AMC,这对于无线网络的演变至关重要.
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