CAIC-Net:通过统一的动态交叉注意力和交叉信号与噪声比对比学习进行强大的无线电调制分类
Teng Wu1,2, Quan Zhu1,2, Runze Mao1,2
1School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100811, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究介绍了CAIC-Net,这是一个用于强大的自动调制分类 (AMC) 的新型网络. 它通过使用动态交叉注意力和交叉SNR对比学习在低信号噪声比 (SNR) 条件下脱而出.
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:
- 自动调制分类 (AMC) 对无线系统至关重要.
- 现有的AMC方法在低信号噪声比 (SNR) 和特征集成方面扎.
- 多个尺度的特征表示通常被低效地集成.
研究的目的:
- 提出CAIC-Net,这是一个强大的AMC网络.
- 在低SNR环境中增强强性.
- 为了提高多尺度特征表示集成的效率.
主要方法:
- 在CAIC-Net中使用双流特征提取器 (ConvLSTM2D和变压器).
- 具有SNR意识的动态交叉注意力单元 (CAU) 可实现自适应融合.
- 交叉SNR对比学习 (CSCL) 模块用于噪声不变表示.
主要成果:
- 在中等至高的SNR中,CAIC-Net实现了具有竞争力的性能.
- 该网络在极低SNR场景中显示出显著的优势.
- 实验验证拟议方法的有效性和通用性.
结论:
- 在复杂的无线环境中,CAIC-Net有效地解决了AMC的挑战.
- 集成动态交叉注意力和交叉SNR对比学习增强了强度.
- 拟议的方法为在不利条件下可靠的调制分类提供了一个有希望的解决方案.
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