通过双路信号变压器学习跨域特征
IEEE transactions on neural networks and learning systems
|January 23, 2024
概括
本研究引入了一种用于自动调制分类 (AMC) 的新跨域信号变压器 (CDSiT). 通过融合不同信号域的特征,CDSiT在复杂环境中提高了可靠性,优于现有的方法.
科学领域:
- 人工智能的人工智能
- 信号处理 信号处理
- 无线通信无线通信
背景情况:
- 深度神经网络 (DNN) 具有先进的自动调制分类 (AMC).
- 在DNN中单域特征学习限制了复杂电磁环境中的可靠性.
- 探索域间信号关联对于强大的AMC至关重要.
研究的目的:
- 为改进AMC提出一种新的跨域信号变压器 (CDSiT).
- 为了提高分类,利用不同信号域之间的潜在关联.
- 解决基于DNN的AMC中单域特征学习的局限性.
主要方法:
- 开发了一个跨域信号变压器 (CDSiT) 架构.
- 引入了隐性特征融合的信号融合瓶 (SFB).
- 在RadioML2016.10A和RadioML2018.01A数据集上进行实验.
主要成果:
- 与现有的AMC方法相比,CDSiT显示出更高的性能.
- 拟议的方法在分类困难的调制模式方面取得了显著的改进.
- 废弃性研究证实了单个CDSiT模块的有效性.
结论:
- 通过利用跨域信号信息,CDSiT为AMC提供了更可靠的方法.
- SFB有效地融合了不同信号领域的互补特征.
- 该CDSiT框架推进了强大的自动调制分类的最新技术.
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