区分嵌入式飞行员辅助自动调制分类为OTFS系统:多域融合方法.
Zhenkai Liu1, Bibo Zhang1, Hao Luo1
1Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
我们开发了一种用于在直角时频空间 (OTFS) 系统中的自动调制分类 (AMC) 的新方法. 这种方法使用双流CNN和独特的试点结构来准确识别调制,即使在具有挑战性的条件下.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 坐标时频空间 (OTFS) 调制对于高流动性环境至关重要,因为它能够减轻多普勒位移.
- 自动调制分类 (AMC) 是OTFS系统的重要预处理步骤,但仍未得到充分探索.
研究的目的:
- 为OTFS系统提出一种新且有效的AMC方法.
- 在具有挑战性的无线条件下提高调制分类的准确性和稳定性.
主要方法:
- 开发了一种双流卷积神经网络 (CNN) 模型,以提取多域信号特征.
- 一个差异化的嵌入式试点结构的设计是为了改善不同调制方案之间的歧视.
主要成果:
- 拟议的AMC方法实现了高分类准确性,特别是在低信号噪声比 (SNR) 条件下.
- 与现有的最先进的基线方法相比,该方法显示出更高的性能.
结论:
- 新的AMC方法为OTFS系统提供了显著的进步,使可靠的调制识别成为可能.
- 双流CNN和差异化的飞行员的组合为未来的无线通信系统提供了强大的解决方案.
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