Radio-Frequency Fingerprint-Tag-Based Device Authentication for Massive Random Access

IoT devices possess a radio-frequency fingerprint (RFF) due to inherent variations in manufacturing, making it device-specific and difficult to alter. This unique RFF, derived from RF signals, is often employed for physical layer authentication (PLA) by matching it against a pre-existing whitelist. However, such schemes struggle to accommodate a massive number of devices. This paper presents a novel new RFF-tag-based approach for device authentication. Essentially, the device sends its binary RRF feature within its message, and the receiver authenticates the device by matching this feature with the one extracted from the received RF signal. To prevent direct transmission of the raw binary feature, the RFF-tag, which is a parity check segment from an error-correcting codeword encoding the feature, is utilized. In the proposed scheme, all the signal processing is confined within the physical layer. The absence of a whitelist within the access point enables scalability to accommodate a massive number of IoT devices. In a simulation involving 500 randomly assigned legitimate devices and 4500 illegitimate devices, it is shown that the average false alarm rate and average misdetection rate are 1.60% and 1.38%, respectively, when the SNR is 22 dB.