合作与欺骗信号共存下的CNN射频指纹识别方法

射频指纹是设备硬件的固有特征，与发射信号本身无关，因此常用于通信抗欺骗中。本文基于射频指纹的原理，采用神经网络对接收机所获得的原始信号样本进行处理，包括I/Q序列、幅度/相位、星座图的二值图和星座图的颜色密度图4种信号表现形式，达到抗欺骗效果。在信干噪比为-30~30 dB 的情况下，信号的识别准确率最高可达99.93%。相较于现有文献，本文所提的基于深度学习的方法可适应不同信干噪比的通信场景，在欺骗信号与合法信号同时存在的复杂通信环境下实现抗欺骗。

Convolutional Neural Network Radio Frequency fingerprint identification method for co-existence of cooperative signal and spoofing signal

The radio frequency fingerprints are inherent features of the device hardware, and will not change with the transmitted signal, therefore they are often used in communication anti-spoofing. In this paper, the neural network is adopted to process the original signal samples obtained by the receiver, including I/Q sequence, amplitude/phase, binary image of constellation diagram and color density diagram of constellation diagram to achieve anti-deception effect. When the signal-to-interference and noise ratio is in the range of -30 dB to 30 dB, the signal recognition accuracy can reach up to 99.93%. Being different from the existing literature, the method can be adapted to the scenes with different signal-to-interference and noise ratios. This research shows that the proposed method is feasible to achieve anti-spoofing in a complex communication environment where spoofing signals and legal signals coexist.