自动调制分类基于CNN-变压器图形神经网络
Dong Wang1,2, Meiyan Lin1,2, Xiaoxu Zhang1,2
1Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了CTGNet,这是一个用于调制分类的新型深度学习模型. CTGNet通过将信号转化为图形结构,提高特征提取以实现准确的信号识别,优于现有方法.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 深度学习,包括卷积神经网络 (CNN) 和循环神经网络 (RNN),显示了对调制分类的希望.
- 传统方法通常使用原始信号或时间频率图像作为输入.
- 图形神经网络 (GNN) 通过将时间序列数据转换为图形结构,提供了一个新的范式.
研究的目的:
- 提出一种新的CNN转换器图形神经网络 (CTGNet),用于增强调制分类.
- 探索将信号数据转化为图形结构的有效性,用于复杂的表示学习.
- 在调制分类任务中实现更高的识别准确性.
主要方法:
- 将滑窗处理应用于原始信号,以创建信号次序.
- 信号次序被重新组织成一个信号次序矩阵.
- 拟议的CTGNet适应地将信号矩阵映射成图形结构,使用GraphSAGE和DMoNPool进行分类.
主要成果:
- 与先进的深度学习技术相比,CTGNet方法实现了最高的识别精度.
- 实验表明CTGNet在捕获关键信号特征方面的显著优势.
- 拟议的方法为调制分类提供了有效的解决方案.
结论:
- 通过利用图形神经网络,CTGNet提供了一种强大的新方法来进行调制分类.
- 该方法在信号数据中发现复杂表示的能力是其关键优势.
- 这项研究推动了信号处理和机器学习应用领域的发展.
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