A Robust Radio Frequency Fingerprint Extraction Method Based on Channel Reciprocity
Bingshu Dong, Aiqun Hu, Jiabao Yu, Hongxia Chen, Zhiyi Shi · 2024
Radio Frequency Fingerprint (RFF) identification is a promising technique for physical layer identification that can enhance wireless security. However, interference of wireless channel characteristics is a key challenge hindering its robustness. To solve this problem, we propose a channel-robust RFF extraction method. First, we design a challenge-response mechanism-based framework to satisfy the uplink and downlink channel reciprocity. Then, we propose a novel RFF extraction method named Quotient of the Estimated Channel State Information (QoECSI) that exploits channel reciprocity to eliminate channel effects. We implemented the QoECSI with the ESP32 development kits that support 2.4GHz Wi-Fi, Experimental results show that the extracted RFF features have high discrimination and long-term stability, and are robust to channel variations and noise. The accuracy rate is higher than 98% when the Signal-to-Noise Ratio (SNR) exceeds 25 dB. Specifically, in a Non-Line-of-Sight (NLOS) scenario with SNR = 35 dB, the average recognition accuracy of cross-validation is 98.56%. In a dynamic scenario where the terminal moves slowly indoors along a fixed route, the highest cross-time-validation accuracy is 98.57%.