Joint multislice and cooperative detection aided RFID method based on deep learning

Abstract

Radio frequency fingerprint identification (RFID) provokes many promising applications of internet of things. Deep learning is considered as one of powerful tools to empower RFID techniques. Recently, several deep learning based RFID methods have been proposed. However, their deep neural networks were trained from the limited length of RF fingerprint samples due to the very high training cost. Hence, existing deep learning based RFID methods are hard to extract full features from the RF fingerprint (RFF) datasets. In order to solve this problem, we propose a joint multislice and cooperative detection aided RFID method based on deep learning. Firstly, acquisition equipment is used to collect RFF signals from seven power amplifiers, which are composed of in-phase and quadrature (IQ) samples with 200,000 sampling points. The IQ samples are sliced and turned into slices with fewer sampling points, which makes them use less training resources, and then they are input into neural network for training. The convolutional neural network (CNN) and convolutional long short-term deep neural networks (CLDNN) are used for training. Secondly, a cooperative detection algorithm is proposed to classify all slices from the same signal to determine the signal category. Finally, experiment results are given to confirm the proposed method in different scenarios. It shows that this method can greatly reduce the training resources, and at the same time maintain a high accuracy.