In the field of Industrial Internet of Things (IIoT) security, Specific Emitter Identification (SEI) plays a crucial role. Recent advancements have seen a rise in the adoption of machine learning (ML) and deep learning (DL) techniques in SEI methodologies, recognized for their impressive effectiveness. However, DL-based SEI methods often incur significant computational costs, making them less suitable for IIoT environments. Similarly, conventional ML-driven SEI approaches depend heavily on feature extraction and employ complex, often redundant classifiers. These methods typically lack in optimizing feature integration and computational efficiency. To overcome these limitations, we introduce an advanced DL-based SEI methodology that focuses on harnessing signal features more effectively. Our method centers around an Adaptive Feature Combination (AFC) strategy, enhanced by an attention mechanism, to develop a more efficient SEI classifier. The essence of our approach is the strategic exploration of adaptive feature combinations, aiming to fine-tune the SEI classifier for peak performance. Simulation results demonstrate that our AFC algorithm outperforms existing SEI methods in both identification accuracy and computational efficiency. This breakthrough offers a viable and promising solution for implementing SEI in IIoT scenarios, achieving heightened effectiveness without sacrificing computational resources.


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    Titel :

    Adaptive Signal Feature-Based Deep Learning for Enhanced Specific Emitter Identification


    Beteiligte:
    Xu, Junzhi (Autor:in) / Chen, Miao (Autor:in) / Wen, Fangqing (Autor:in) / Lu, Gejiacheng (Autor:in) / Hu, Lifan (Autor:in) / Wang, Yu (Autor:in) / Lin, Yun (Autor:in) / Gui, Guan (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    751673 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch