Design And Implementation Of Small Sample Identification Methods For Radio Frequency Fingerprint

With the advent of the 5G communication era,it is very essential to ensure the communication security between devices.The hardware features extracted by analyzing the small differences of radio frequency（RF）signals can be used as the unique RF fingerprint of each device,so that the RF fingerprint recognition technology can provide a more secure and reliable authentication mechanism for the device at the physical layer.The existing RF fingerprint identification technologies are all implemented on dense data sets,which are somewhat helpless when faced with the problem of small samples.Especially in some special environments,such as drone and other equipment in a relatively concealed environment,it is impossible to obtain numerous of their signals for RF fingerprint identification.In order to solve the problem of small samples of RF fingerprints,this thesis proposes to apply the few-shot learning methods to RF fingerprint recognition technology,and studies the identification methods of small samples for RF fingerprints.In this thesis,by analyzing the shortcomings of the existing RF fingerprint recognition technology,it is found that the problem of overfitting is easy to occur when faced with small sample data sets.At present,the existing few-shot learning methods include meta-learning,transfer learning,etc.,which are mainly used in the field of image and text.Therefore,in view of the above issues,this thesis improves the existing meta-learning network model,and designs and implements two models.The first is the improvement of the matching network model.In order to extract more effective RF fingerprint features,combined with the characteristics of I/Q signal,the embedding function structure of the matching network is modified.And the Euclidean distance formula is used as the attention kernel function to calculate the similarity of sample features,so that the model can better distinguish the difference between I/Q signal samples,thereby ensuring the accuracy of model recognition.The second is to combine the advantages of transfer learning and meta-learning to build a meta-transfer learning model.This model introduces a deep neural network into a meta-learning model through transfer learning and scales the model parameters to suit the RF fingerprinting few-shot task,thereby improving the model recognition accuracy.The experimental results show that the few-shot learning method can be applied to the small sample recognition of RF fingerprints.Compared with before improvement,the improved matching network model in this thesis has well performance in recognition effect and faster training speed.At the same time,it is also verified that the deep neural network can be applied to the meta-learning model,and the meta-transfer learning model performs better in the recognition of small samples of RF fingerprints.Finally,an equipment signal analysis system is designed and implemented based on the above two models to help quickly identify the equipment to which the RF signal belongs.It also lays the foundation for the identity authentication of wireless communication equipment under the subsequent network attack and defense,which has certain strategic significance.