Research On Recognition Technology Of Communication Transmitting Source Based On Power Amplifier Modeling

The increasing number of illegal frequency uses has made the spectrum environment more complicated.Frequently,signals from different sources and different properties share a section of spectrum,making normal communication impossible.Aiming at the above problems,in order to improve the spectrum security-oriented interference investigation ability,by exploring the intrinsic nonlinearity of the wireless transmitting source,that is,the nonlinear effect of the individual wireless transmitter on the transmitted signal,the differential characteristics of the openness and fading of the propagation channel are used.The technique of judging which specific transmitter target it came from by receiving the signal has become a research hotspot.This article mainly researches the individual characteristics of the transmitting source based on the process of identifying the individual transmitting source,and then proposes a corresponding extraction method based on this,and then combines the classification decision-making method of machine learning to realize the individual identification of the transmitting source.The main work of this article is as follows:Firstly,the reasons for the non-linear characteristics of the components of the emission source and the commonly used distortion representation methods are introduced in detail.Then,based on the processing flow framework of the individual identification of the emission source,the main technical methods of signal feature extraction and classification identification methods are described.The limiting factors of Volterra series are given from the perspective of nonlinear time-invariant systems,and several simplified models based on Volterra series models are introduced.By analyzing the advantages and disadvantages of the existing models,a simplified generalized polynomial model(SGMP)is proposed.This model is used to model the power amplifier.The simulation proves that its performance is higher than other simplified models,which can reduce the signal-to-noise ratio.At 20dB,the accuracy similar to the Volterra series model is obtained.Based on this,an individual identification algorithm of transmitting source based on SGMP model of power amplifier was proposed,and the random forest classification algorithm based on Renyi entropy weighted sampling improved in this paper was used as the decision criterion.Simulation experiments show that the method can achieve a recognition accuracy of 90%when the received signal-to-noise ratio is greater than 10dB.Compared with other models and decision-making methods,it also has certain performance advantages.From a holistic perspective,the nonlinear characteristics of the entire transmitter system are studied.With the help of analysis tools and methods of chaos theory,the chaos characteristics in the nonlinear system are verified by simulation.Based on this,a method of individual emitter identification based on the combination of normalized permutation entropy and natural measures is proposed.The simulation results show that the method can achieve high recognition accuracy under the condition that the adjustable parameters are suitable.