Model-Based RF Fingerprint Extraction Approach for Robust IoT Device Identification

Radio frequency fingerprint identification (RFFI) leverages signal distortions caused by hardware impairments to identify transmitters, thereby enhancing IoT security. However, current radio frequency fingerprints (RFFs) modelings typically focus on partial hardware impairments, risking incomplete RFF extraction and limited RFF understanding. This study aims to refine the modeling of RFFs and guide the development of robust and accurate RFFI approaches based on this model. Specifically, we propose a comprehensive time-domain signal distortion model based on hardware impairments in wireless transmission circuit components, revealing that RFFs can be categorized into two types: fine-grained and coarse-grained RFFs. The former encompass localized distortions such as mismatches, inter-symbol interference and non-linear distortions; the latter relate to global features, including frequency spurs, phase noise, and crystal oscillator frequency offset. Subsequently, we analyze the impact of interference on the RFF model and proposes necessary methods to mitigate the interference. Combining the comprehensive analysis of the RFF model and interference, we summarize three primary characteristics of RFFs: multi-scale, fixedness, and ubiquity. These characteristics indicate that convolutional neural networks (CNNs) from the visual domain cannot be directly transferred or simply adapted in terms of input data shape for application in RFFI. Therefore, we propose an enhanced CNN architecture with grouped convolutions and channel fusion modules for effective RFF extraction. To demonstrate the generalizability of our approach, we conduct extensive experiments using three public IOT signal datasets. Experimental results demonstrate that our method exhibits excellent identification performance and robustness against interference across various environments.