Radio Frequency Fingerprinting Identification Method Research Based On Steady State Signals

The security of wireless communication is a matter of widespread concern.In the field of communication network security,identifying illegal devices is a key problem.Radio frequency fingerprinting gets a widespread of research,because radio frequency fingerprinting feature is difficult to imitate by software,while it uniquely represent the devices.It has been proved that,in the process of equipment production,even the same type of equipment produced by the same manufacturer will have different characteristics due to the difference in the proportion of material components,just like everyone has a different fingerprint.These physical layer characteristics,called radio frequency fingerprinting features,will be reflected in communication signals.These features are unique and can not be forged.They can be extracted by analyzing the received radio frequency signals.Therefore,by recognizing the authorized devices from the invasion devices through extracting and classifying radio frequency fingerprinting features,the security and privacy of the communication can be enhanced.In this paper,we focus on steady state signals and extract the radio frequency fingerprinting features to identify radio frequency devices.The main research results are shown as follows:Considering the mechanism and characteristics of radio frequency fingerprinting,the radio frequency fingerprinting identification system model is established.In this paper,a novel method extracting the energy entropy and color moment of bispectrum as radio frequency fingerprinting features,employing the support vector machine(SVM)as the classifier is proposed.It is called BEECM(Bispectrum-based method using energy entropy and color moments).The simulate results prove the effectiveness of the proposed BEECM method in theoretical,especially when SNR(Signal to Noise Ratio)is low.In addition,it shows that the method has a certain advantage in the identification accuracy.When SNR= OdB,the correct rate is nearly 80%.With the increase of SNR,the accuracy of classification increased,achieving above 95%at 20dB.What’s more,in this paper,software radio system consists of software radio development platform GNU Radio and the 4 USRPs from the same manufacturer of same type are employed as the actual signal transceiver.3 USRPs are acted as a signal sending devices,1 USRP is used as a signal receiving device.Through the processing of the received signals,the radio frequency fingerprinting features are extracted respectively.Through training and testing,we get the classification accuracy.The experimental results further demonstrate the advantages and feasibility of the BEECM method proposed in this paper.