对于非直角信号的深度学习辅助调制识别
Jiaqi Fan1, Linna Wu2, Jinbo Zhang3
1School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了在非直角系统中用于自动调制识别 (AMR) 的深度学习. 新的方法提高了对下链和上链传输的信号分类准确性.
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:
- 自动调制识别 (AMR) 对于无发射器辅助的信号处理至关重要.
- 由于信号叠加,现有的AMR方法与非对角信号作斗争.
- 深度学习为复杂的信号识别提供了一个有前途的数据驱动方法.
研究的目的:
- 为非直角下链和上链信号开发基于深度学习的高效AMR方法.
- 解决非直角传输系统中叠加信号所带来的挑战.
- 在各种通信场景中提高AMR准确性和稳定性.
主要方法:
- 提出了一种双向长期短期记忆 (BiLSTM) 网络,用于下链AMR的转移学习.
- 开发了一个时空融合网络,用于上链AMR的注意力机制.
- 针对非直角信号叠加特性的优化网络架构.
主要成果:
- 该BiLSTM方法有效地学习下链信号的不规则信号星座.
- 时空融合网络有效地提取了上链AMR的特征.
- 深度学习方法在非直角系统中明显优于传统方法.
- 在3层上链场景中实现了~96.6%的准确性,比CNN有19%的改进.
结论:
- 基于深度学习的AMR对非直角传输系统非常有效.
- 拟议的BiLSTM和时空融合网络为AMR挑战提供了强大的解决方案.
- 这些先进的方法为提高未来无线通信性能铺平了道路.
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