Enhanced Specific Emitter Identification With Limited Data Through Dual Implicit Regularization

Abstract

Specific Emitter Identification (SEI) is a critical technology for physical layer authentication in wireless communications and the Internet of Things. Leveraging the inherent and hard-to-forge characteristics of Radio Frequency Fingerprinting (RFF), SEI has gained significant attention. Recent advancements in deep learning have propelled SEI methods to new heights of identification performance. However, these methods are often constrained by their reliance on large datasets, posing challenges in real-world scenarios with limited samples. Addressing this issue, this paper proposes an enhanced SEI approach tailored for limited sample environments, employing Double Implicit Regularization (DIR). Our proposed method, DIR-MRAN, utilizes a Multi-Scale Residual Attention Network (MRAN) to extract features effectively from limited samples. The DIR strategy enhances model generalizability by incorporating Sample-wise Implicit Regularization (SIR) and Label-wise Implicit Regularization (LIR), which respectively facilitate sample expansion and label smoothing. We evaluated DIR-MRAN on two real-world datasets, achieving an impressive 95.34% accuracy on the PA dataset and outperforming comparative methods by 26.4% on the ADS-B dataset.