基于深度学习的数字调制信号的分类,使用囊网络和循环积器
John A Snoap1, Dimitrie C Popescu1, James A Latshaw1
1Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一种新的深度学习方法,使用囊网络和循环累积物来分类数字调制信号. 这种新的方法显示出比现有技术更高的性能.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 数字调制信号的分类对于各种通信系统至关重要.
- 传统的方法经常与复杂的信号环境和概括性作斗争.
- 深度学习为提高分类准确性和稳定性提供了潜力.
研究的目的:
- 提出一种基于深度学习的新方法来对数字调制信号进行分类.
- 为了利用带有循环累积 (CC) 功能的囊网络 (CAP) 来进行增强的信号分类.
- 评估拟议方法的分类性能和概括能力.
主要方法:
- 使用循环静止信号处理 (CSP) 对循环累积 (CC) 特性进行盲目估计.
- 将估计的CC特征输入囊网络 (CAP) 进行培训和分类.
- 在两个具有不同信号生成参数的不同数据集上测试方法.
主要成果:
- 拟议的CAPs和CCs方法显著优于基于CSP的传统分类器.
- 与使用I/Q数据的CNN和RESNET等替代深度学习模型相比,它也表现更好.
- 该方法在具有不同生成参数的数据集中展示了强大的概括能力.
结论:
- 囊网络与循环累积剂相结合,为数字调制信号分类提供了强大而有效的方法.
- 这种深度学习方法为现有的分类技术提供了强大的替代方案.
- 这些发现突显了功能工程的潜力,其中包括用于信号处理任务的先进深度学习架构.
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