Knowledge Transfer for Specific Emitter Identification: Fundamentals, Applications, and Meta-Learning Perspectives

Radio-Frequency Fingerprinting (RFF)-based Specific Emitter Identification (SEI) enables device-level recognition by exploiting hardware-induced signal characteristics. However, practical wireless channels often mix RFFs with channel responses, receiver effects, and environmental interference, leading to cross-domain performance degradation. Existing transfer approaches such as parameter-efficient fine-tuning and distribution alignment can partially mitigate this gap, but they do not explicitly preserve the relational structure among emitter fingerprints, often resulting in overfitting or negative transfer under few-shot conditions. This article reviews knowledge transfer for wireless intelligence, covering its fundamental concepts, representative methods, and key applications. We further present a Meta-Learning-Guided Knowledge Transfer Network (MLGKFN) for SEI, which represents RFF-related relations through graph structures and learns transferable meta-knowledge for rapid adaptation to new environments. Specifically, MLGKFN encodes each 1,024-point IQ signal into eight signal-dependent node embeddings, dynamically constructs sparse RFF relations using attention with Top-k (k=3) sparsification, and enables rapid few-shot adaptation across channel conditions through episodic meta-training. By combining relational knowledge transfer with meta-learning, this framework provides a promising direction for building wireless intelligence systems with stronger generalization and sample efficiency.