Cross-Domain Generalization for Specific Emitter Identification With Unseen Signals via Fourier Phase and MMD Features

Specific Emitter Identification (SEI) in wireless communications enhances security by distinguishing devices through their unique RF signal impairments. Deep learning (DL) techniques have become instrumental and have attracted considerable focus in this domain. Nevertheless, significant challenges arise from pronounced domain distribution disparities and the lack of labeled signal for target domain emitters, complicating cross-domain individual emitter identification. This paper proposes an innovative domain generalization framework for SEI, which exploits both intra-domain and inter-domain invariant features to enhance cross-domain robustness. These features significantly enhance the model’s generalization capabilities: intra-domain invariants are captured through the use of Fourier phase information and knowledge distillation, while inter-domain invariants are derived via Maximum Mean Discrepancy (MMD) feature alignment. The proposed methodology exhibits strong performance across three domain generalization scenarios, utilizing datasets SUI-1, SUI-3, and SUI-5. Each dataset comprises signals from seven distinct transmitters, recorded over varying fading channels. This approach achieves an approximate 5% improvement over state-of-the-art SEI domain adaptation methods, highlighting its superior generalization capability. The code and datasets for the paper can be found at: https://github.com/ZHR-HEU/Cross-Domain-Generalization-for-SEI.