Diff-ADF: Differential Adjacent-dual-frame Radio Frequency Fingerprinting for LoRa Devices

Abstract

Nowadays, LoRa radio frequency fingerprinting has gained widespread attention due to its lightweight nature and difficulty in being forged. The existing fingerprint extraction methods are mainly divided into two categories, i.e., deep learning-based methods and feature engineering-based methods. Deep learning-based methods have poor robustness and require significant resource costs for model training. Although feature engineering-based methods can overcome these drawbacks, the features it commonly uses, such as carrier frequency offset (CFO) and phase noise, lack sufficient discriminative power. Therefore, it is very challenging to design a radio frequency fingerprinting solution with high-accuracy and stable identification performance. Fortunately, we find that the differential phase noise of adjacent dual frames possesses excellent discriminative power and stability. Then, we design the corresponding radio frequency fingerprinting solution called Diff-ADF, which utilizes a classifier with differential phase noise as the primary feature, complemented by the use of CFO as an auxiliary feature. Finally, we implement the Diff-ADF and conduct experiments in real environments. Experimental results demonstrate that our proposed solution achieves an accuracy of over 90% on training and test data collected from different days, which is significantly superior to deep learning-based methods. Even in non-line-of-sight environments, our identification accuracy can still reach close to 85%.