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Avoiding Shortcuts: Enhancing Channel-Robust Specific Emitter Identification via Single-Source Domain Generalization
Yu Wang; Tomoaki Ohtsuki; Zhi Sun; Dusit Niyato; Xianbin Wang; Guan Gui
By extracting radio frequency (RF) fingerprints from received signals, specific emitter identification (SEI) becomes a promising technique for physical layer identification of wireless devices. Recently, channel-robust SEI has attracted increasing attention due to the weak robustness exhibited by deep learning (DL)-based SEI methods in cross-channel conditions. To address these limitations, we propose a novel channel-robust SEI framework based on single-source domain generalization (SDG). Initially, we analyze the weak robustness of existing SEI methods from the perspective of the “shortcut learning” phenomenon in DL. Shortcut learning may lead traditional SEI methods to prioritize easily-mined, yet transient, channel characteristics in signal samples, rather than focusing on the more stable RF fingerprints derived from hardware differences. Next, from the perspective of SDG, we outline the optimization goal to rectify the shortcut learning in SEI. Inspired by this optimization goal, we then propose a channel-robust SEI method. This method consists of feature embedding through a multi-scale convolutional attention network (MSCAN), domain expansion using random overlay augmentation (ROA) to generate multiple virtual domains, and dual alignment strategy based on contrastive learning. Specifically, supervised contrastive learning is implemented for category-wise alignment, while supervised contrastive adversarial learning is utilized for domain-wise alignment. This dual alignment strategy can optimize the MSCAN to learn discriminative and domain-invariant feature representations, thereby enhancing the robustness of SEI. Simulation experiments on the ORACLE dataset and the WiSig dataset have demonstrated the superiority of our method compared to state-of-the-art techniques. The codes can be downloaded from GitHub1.
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