Abstract:
Radio frequency fingerprinting identification (RFFI) is a technology that uses signal processing techniques to extract the characteristics of collected wireless signals t...Show MoreMetadata
Abstract:
Radio frequency fingerprinting identification (RFFI) is a technology that uses signal processing techniques to extract the characteristics of collected wireless signals to enable the individual identification of devices. Over the past few years, deep learning (DL) has made great achievements in the field of signal identification, and the research on the RFFI methods has made great progress. To address the issue of fixed-size input data required by many neural networks, such as convolutional neural network (CNN) and multilayer perceptron (MLP), this paper proposes a transformation method that converts variable-length signals into contour stella constellation (CSC). In addition, in the most DL-based the RFFI methods, a global analysis of the entire radio frequency signal is required, which demands a large amount of calculation and is easily affected by changes in the propagation environment. We propose an incremental learning (IL)-based the RFFI approach, which enables dynamic updating of the model and improves its identification and generalization capabilities. Experimental results show that our proposed method shows excellent performance in processing data streams in real time and the identification accuracy finally can reach 89.14%. The codes of this paper can be downloaded from https://github.com/sfuaena/CSC-KD.
Date of Conference: 20-22 October 2023
Date Added to IEEE Xplore: 12 February 2024
ISBN Information: