合作与欺骗信号共存下的CNN射频指纹识别方法

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

摘要:

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。

Abstract:

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.