Embedded RF Fingerprint Interpretation: Multi-Channel Complex Residual Networks with Adaptive Sphere Space Decision Boundaries

Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature (I/Q) signals, challenges persist due to signal-type confusion and background noise interference. To address those limitations, this paper proposes a multi-channel contrast prediction coding and complex-valued residuals network (MCPC-MCVResNet) framework. This model employs contrast prediction techniques to directly extract discriminative features from electromagnetic signal sequences, effectively capturing both amplitude and phase information within I/Q data. A core innovation of this approach is the sphere space softmax (SS-softmax) loss, which optimizes intra-class clustering density of while establishing well-defined boundaries between distinct emitters. The SS-softmax mechanism significantly enhances the model's capacity to discern subtle variations among radiation emitters. Experimental results demonstrate superior identification accuracy, rapid convergence, and exceptional robustness in low signal-to-noise ratio environments.