Channel-Robust RF Fingerprint Identification Using Multi-Task Learning and Receiver Collaboration

Abstract

Robust radio frequency fingerprint identification (RFFI) is crucial for physical layer authentication, while it suffers from channel effects and requires extra overhead to increase recognition accuracy (RA). To address this, an efficient channel-robust RFFI scheme is proposed, employing a specialized multi-task learning (MTL) framework to direct the neural network (NN) toward extracting channel-robust features. In addition, receiver collaboration (RC) is utilized for data augmentation and output calibration. Experimental results demonstrate that the RA is significantly increased from 51.72% to 99.97% when using the open-resource Wi-Fi signal datasets collected from different time periods. Meanwhile, the requirements for extra data transmission, NN structure, and feature crafting in the inferring stage are dramatically simplified.