基于多尺度窗口和区域注意力残差网络的无线电力终端身份识别方法  

Wireless Power Terminal Identification Method Based on Multiscale Windowed Deep Residual Network

作　　者：赵洪山[1] 孙京杰 彭轶灏 赵仕策 许俊洋 王羽丰 Zhao Hongshan;Sun Jingjie;Peng Yihao;Zhao Shice;Xu Junyang;Wang Yufeng(Department of Electrical Engineering North China Electric Power University,Baoding 071000 China;State Grid Nanchang Power Supply Company,Nanchang 330000 China)

机构地区：[1]华北电力大学电力工程系,保定071000 [2]国网南昌供电公司,南昌330000

出　　处：《电工技术学报》2023年第1期107-116,共10页Transactions of China Electrotechnical Society

摘　　要：针对现有无线通信设备信号识别方法需对信号进行域变换、增加网络输入数据维数的问题,该文提出基于多尺度窗口区域注意力残差网络的无线电力终端身份识别方法。首先,通过所提多尺度窗口模块完成信号前导码在各个周期尺度下的信息交互,使网络能够直接处理并识别原始无线通信信号数据;然后,设计区域注意力模块,以显著特征区域均值为评价指标对通道资源进行重新分配,提高了网络对信号局部特征的学习能力;最后,以池化分类器替代全连接层,采用Adam优化器进行梯度更新完成训练过程。实际采集无线信号数据实验结果表明,设计的各模块可显著提升网络的训练与识别性能,相同型号设备识别准确率提高至97.316%,非法设备的检测率达82.8%,可有效增强电力系统的无线通信安全。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 improvem

关 键 词：无线通信安全 残差网络 身份识别 物理层安全 注意力机制 

分 类 号：TN92[电子电信—通信与信息系统]