Radio frequency fingerprint identification (RFFI) technology identifies the emitter by extracting one or more unintentional features of the signal from the emitter. To solve the problem that the traditional deep learning network is not highly adaptable for the contour features extracted from the signal, this paper proposes a novel RFFI method based on a deformable convolutional network. This network makes the convolution operation more biased towards the useful information content in the feature map with higher energy, and ignores part of the background noise information. The proposed blind identification method requires less information and no training sequences and pilots, Thus, it achieves energy and spectrum efficient radio communications. Simulation verifies that the proposed method can achieve better recognition performance and is beneficial for green radios.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Energy and Spectrum Efficient Radio Frequency Fingerprint Intelligent Blind Identification


    Beteiligte:
    Liu, Mingqian (Autor:in) / Yan, Zhiwen (Autor:in) / Zhang, Junlin (Autor:in)


    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    547045 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Investigating Sparse Neural Networks for Radio Frequency Fingerprint Identification

    Bothereau, Emma / Chillet, Alice / Gerzaguet, Robin et al. | IEEE | 2024



    Improvement of Radio Frequency Fingerprint Portability for Wi-Fi Adaptor Identification

    Yang, Hsin-Hung / Wang, Tzu-Chia / Yang, Yi-Ju et al. | IEEE | 2021


    Robust Radio Frequency Fingerprint Identification for UAVs During Fast Fading Channels

    Wang, Zhaorui / Shi, Xu / Hua, Xiangyang et al. | IEEE | 2024