Joint Radio Frequency Fingerprints Identification via Multi-antenna Receiver

TL;DR

This paper introduces three novel radio frequency fingerprint identification schemes that leverage multi-antenna receiver distortions for enhanced passive security authentication in IoT, demonstrating improved robustness and effectiveness.

Contribution

It proposes three new RFF identification schemes that utilize independent oscillator distortions at the receiver, filling a gap in existing passive authentication methods.

Findings

GDFWS effectively filters receiver distortions and channel noise.

Proposed schemes outperform traditional methods in robustness.

Theoretical analysis confirms DFS's filtering capability.

Abstract

In Internet of Things (IoT), radio frequency fingerprints (RFF) technology has been widely used for passive security authentication to identify the special emitter. However, few works took advantage of independent oscillator distortions at the receiver side, and no work has yet considered filtering receiver distortions. In this paper, we investigate the RFF identification (RFFI) involving unknown receiver distortions, where the phase noise caused by each antenna oscillator is independent. Three RFF schemes are proposed according to the number of receiving antennas. When the number is small, the Mutual Information Weighting Scheme (MIWS) is developed by calculating the weighted voting of RFFI result at each antenna; when the number is moderate, the Distortions Filtering Scheme (DFS) is developed by filtering out the channel noise and receiver distortions; when the number is large enough, the Group-Distortions Filtering and Weighting Scheme (GDFWS) is developed, which integrates the advantages of MIWS and DFS. Furthermore, the ability of DFS to filter out the channel noise and receiver distortions is theoretically analyzed at a specific confidence level. Experiments are provided when both channel noise and receiver distortions exist, which verify the effectiveness and robustness of the proposed schemes.