异构传输协议干扰下的校准射频指纹识别

中文摘要 AI 辅助

射频指纹识别是一种频谱监测技术，基于发射信号中固有的硬件缺陷来识别特定发射机。尽管该技术已被广泛研究，但现有研究几乎只考虑单一发射机同时发射的场景，限制了其实际应用。在本工作中，我们进一步研究射频指纹识别，考虑同信道干扰，即多个发射信号在时间和频率上重叠并相互干扰。具体而言，我们将该问题建模为多标签分类问题，并采用一维卷积神经网络（CNN）。此外，模型经过校准，从而推导出标签概率的置信阈值，并保证平均假阴性数量的上界，为不遗漏真实的频谱政策违规提供一定置信度。所提方法使用POWDER 5G测试平台上的真实数据进行验证，该平台上的设备发射802.11a（Wi-Fi）、4G LTE和5G NR波形。结果表明，根据信道条件，校准后的准确率最高可达97%，最低为73%。同时，针对不同平均假阴性上界进行校准，实现了约（1-校准假阴性）的微召回率，且校准对分布外干扰具有鲁棒性，展示了所提方法在高竞争无线环境中的实际应用潜力。

英文摘要

Radio Frequency(RF)-Fingerprinting is a spectrum monitoring technique that identifies specific transmitters based on hardware impairments imprinted within the emitted signal. Although widely researched, studies almost exclusively consider scenarios where only one transmitter is emitting at a time, limiting real world applicability. In this work, we further the study of RF-Fingerprinting by considering co-channel interference, with multiple emitted signals interfering with each other, overlapping in time and frequency. Specifically, we formulate this problem as a multi-label classification problem and employ a 1D convolutional neural network (CNN). Furthermore, the models are calibrated such that the confidence thresholds for the label probabilities are derived, with guarantees on the upper bound on the average number of False Negatives, providing a degree of confidence in not missing a true spectrum policy violation. The proposed method is validated using real world data from the POWDER 5G testbed on devices transmitting 802.11a(Wi-Fi), 4G LTE, and 5G NR waveforms. The results show accuracy as high as 97% and as low as 73% after calibration depending on channel conditions. Also calibrating for various average false negatives upper bounds achieves micro recall scores of approximately (1 - calibrated false negatives) with the calibration robust to out-of-distribution interference, demonstrating the potential of the proposed method in a realistic high contention wireless environment

发表机构

• Prairie View A&M University, Texas A&M University System（普雷里维尤农工大学，德克萨斯农工大学系统）

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