Wireless Power Terminal Identification Method Based on Multiscale Windowed Deep Residual Network
Zhao Hongshan1, Sun Jingjie1, Peng Yihao2, Zhao Shice1, Xu Junyang1, Wang Yufeng1
1. Department of Electrical Engineering North China Electric Power University Baoding 071000 China; 2. State Grid Nanchang Power Supply Company Nanchang 330000 China
Abstract：The technique of wireless terminal identification based on the differential characteristics of wireless signals is currently an important physical layer security mechanism. However, traditional wireless signal identification methods generally require signal-signal domain conversion. Therefore, the dimensionality of the data and the arithmetic power requirements are enhanced. This can increase the application cost of this security mechanism. To solve this problem, a residual network-based wireless power terminal identification method is proposed. With the designed multiscale window module and area attention module, it can directly process the signal raw data to accurately identify the legal device identity and illegal device detection. First, the proposed multiscale window module completes the information interaction of the signal precursor code at each cycle scale, enabling the network to directly process and identify the raw wireless communication signal data. Then, the regional attention module is designed to reallocate channel resources with the mean value of significant feature regions as the evaluation index, which improves the network's ability to learn local features of signals. Finally, a pooling classifier is used to replace the fully connected layer, and the Adam optimizer is used for gradient update to complete the training process. In this model, the multiscale window module makes use of the leading code subframe feature, which can directly process the original signal data and greatly improve the learning performance recognition effect. The experimental results on the actual collected wireless terminal signal data show that the multiscale module improves the recognition accuracy by 31% compared with the traditional residual network due to the comprehensive consideration of the information of each subframe of the leading code signal. The recognition accuracy and learning performance of the network are significantly improved, which verifies the effectiveness of the module on network performance improvement. The addition of the regional attention mechanism further improves the recognition accuracy while improving the training performance. It is verified that the region attention mechanism can effectively improve the performance of the network in recognizing signal distortion features. The recognition accuracy is up to 97.316% for 30 identical models of commercial devices. Also, the maximum value of the output probability corresponding to the label is selected as the identification result of the identity, and the threshold of the output probability is defined as the confidence level, which can also detect the illegal devices while further scientific evaluation. Five experiments of uninvolved training devices are selected for illegal detection, and the results show that when the confidence level is 99%, the detection rate of illegal devices reaches 82.8%. The following conclusions can be drawn from the analysis of the experimental results: ① The constructed multiscale module avoids the loss of local difference features caused by the analysis of the signal as a whole because it considers the information of each subframe of the leading code signal, which significantly improves the training performance and recognition accuracy. ② The proposed regional attention mechanism further enhances the learning of regional difference features by dividing the channel into regions, which achieves a significant improvement in the training performance and This achieves further improvement of training performance and recognition accuracy. ③ The proposed identification method based on multi-scale windowed regional attention residual network achieves self-learning of the difference features of the original I/Q data of wireless signals. The recognition accuracy of the same model device can reach 97.316%, and the recognition rate of illegal devices reaches 82.8%.
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2023, Vol. 38 
