Time-frequency localization based intelligent wideband spectrum sensing is essential for achieving precise dynamic spectrum access and management. Currently, object detection based detectors can achieve excellent time-frequency localization performance in a simple electromagnetic environment. However, the overlapping of signals in the time-frequency domain can corrupt signal features and degrade detector performance. In this paper, we propose a transformer based robust time-frequency localization (TRTFL) detector that fully extracts the features of the time-frequency domain correlation to improve its robustness in solving the aforementioned problem. Furthermore, to exploit the convolution operation for mining fine-grained features, we embed a convolutional layer with a small kernel in the transformer block. Finally, simulation results validate the advantages of TRTFL compared to existing detectors and demonstrate its robustness for overlapping signals in spectrogram.


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    Titel :

    TRTFL: A Transformer Based Robust Time-Frequency Localization Detector for Spectrogram with Overlapping Signals


    Beteiligte:
    Zhao, Runyi (Autor:in) / Ruan, Yuhan (Autor:in) / Xu, Huacheng (Autor:in) / Li, Tao (Autor:in) / Zhang, Rui (Autor:in) / Yang, Dong (Autor:in) / Li, Yongzhao (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    373284 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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