Leveraging MIMO Transmit Diversity for Channel-Agnostic Device Identification
Leveraging MIMO Transmit Diversity for Channel-Agnostic Device Identification
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DOI：
10.1109/icc45855.2022.9838976
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
N. Basha;B. Hamdaoui;K. Sivanesan
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文献类型：
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作者：
N. Basha;B. Hamdaoui;K. Sivanesan
在大规模网络（例如物联网网络）中，无线设备的准确识别对于实现自动化网络访问监控和经过认证的数据通信至关重要。射频指纹识别通过利用射频组件独特的制造缺陷作为设备识别的一种潜在解决方案应运而生。尽管深度学习在基于硬件缺陷对设备进行分类方面已被证明是有效的，但由于信道变化，训练后的模型性能不佳。也就是说，尽管使用同一时期生成的数据对神经网络进行训练和测试能够实现可靠的分类，但使用不同时间生成的数据进行测试会大幅降低准确性。据我们所知，我们是首个提出利用多输入多输出（MIMO）能力来减轻信道影响并提供一种对信道有适应性的设备分类方法的。对于所提出的技术，我们表明，对于瑞利信道，当模型在同一信道上进行训练和测试时，由MIMO实现的盲部分信道估计可将测试准确性提高多达40%，而当模型在与训练所用信道不同的信道上进行测试时，可将准确性提高多达60%。
The accurate identification of wireless devices is critical for enabling automated network access monitoring and authenticated data communication in large-scale networks; e.g., IoT networks. RF fingerprinting has emerged as a potential solution for device identification by leveraging the transmitter unique manufacturing impairments of the RF components. Although deep learning is proven efficient in classifying devices based on the hardware impairments, trained models perform poorly due to channel variations. That is, although training and testing neural networks using data generated during the same period achieve reliable classification, testing them on data generated at different times degrades the accuracy substantially. To the best of our knowledge, we are the first to propose to leverage MIMO capabilities to mitigate the channel effect and provide a channel-resilient device classification. For the proposed technique we show that, for Rayleigh channels, blind partial channel estimation enabled by MIMO increases the testing accuracy by up to 40% when the models are trained and tested over the same channel, and by up to 60% when the models are tested on a channel that is different from that used for training.