交叉接收机无线电频率指纹识别:一种无源适应方法
Jian Yang1, Shaoxian Zhu2, Zhongyi Wen2
1School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100080, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究引入了一种用于无线电频率指纹识别 (RFFI) 的新方法,该方法将模型调整为没有原始数据的新设备. 相反的无源交叉接收器网络 (CSCNet) 在挑战交叉接收器场景中提高了准确性和稳定性.
科学领域:
- 网络安全和电信领域
- 信号处理和机器学习
背景情况:
- 无线电频率指纹识别 (RFFI) 使用独特的信号特征来识别设备.
- 深度学习提高了RFFI准确性,但由于硬件变化,在交叉接收机模型部署方面面临挑战.
- 数据隐私和传输限制阻碍了将RFFI模型转移到新接收器.
研究的目的:
- 介绍无源交叉接收器RFFI (SCRFFI) 问题:调整RFFI模型以适应没有原始训练数据的新接收器.
- 提出一种新的方法,即对比的无源交叉接收网络 (CSCNet),以使用未标记的数据进行有效的模型调整.
- 提高RFFI识别的准确性和稳定性,以应对接收器变化和数据限制.
主要方法:
- 开发了CSCNet,利用对比学习进行模型适应,使用未标记的接收器数据.
- 实现了三支损失函数:信息损失,伪标签自我监督损失和对比学习损失.
- 对概括性能进行了理论分析,并通过对现实世界数据集的广泛实验进行了验证.
主要成果:
- CSCNet有效地减轻了接收器变化和源数据缺失的影响.
- 在现实的噪音和通道条件下,在识别精度和稳定性方面取得了显著的改进.
- 与现有方法相比,平均提高了至少13%,在具有挑战性的跨接收器适应任务中增加了47%.
结论:
- CSCNet为SCRFFI问题提供了一个强大的解决方案,可以在没有源数据的情况下有效地调整RFFI模型.
- 拟议的方法在多样化和具有挑战性的部署场景中显著提高了RFFI性能.
- 这项工作解决了在RFFI模型部署中的关键数据隐私和传输问题.
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