Compared with the traditional spectrum sensing methods that can only detect the presence or absence of signals, deep learning-based time-frequency localization (TFL) methods can obtain two-dimensional time-frequency information (TFI). However, for time-frequency domain overlapped signals, TFL methods can only obtain the contour TFI of the whole time-frequency block (TF-Block), but cannot obtain the TFI and corresponding relationship of each component signal contained in the TF-Block, which is named index ambiguity here. To use spectrum resources more efficiently, it is need to mine the usage of time-frequency resources in multidimensional space as much as possible, such as the time-frequency resources occupation and direction-of-arrival (DoA) of each component signal, which can help the users to avoid interference in spectrum reuse. In this paper, a processing framework is designed to eliminate the index ambiguity. Based on the result of TFL, rank features are extracted using the sliding window method to characterize the signal property, and then a signal segmentation algorithm is designed to obtain the concrete composition of overlapped signals. Finally, the DoA of each component signal is obtained based on the signal segmentation information. Simulation results demonstrate that the proposed method can efficiently and accurately extract multidimensional information to eliminate the index ambiguity of overlapped signals.


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    Title :

    Elimination of Index Ambiguity for Overlapped Signals in Spectrum Sensing


    Contributors:
    Zhou, Shuai (author) / Wei, Dishan (author) / Li, Tao (author) / Li, Yongzhao (author) / Zhang, Rui (author) / Ruan, Yuhan (author) / Yang, Dong (author)


    Publication date :

    2024-06-24


    Size :

    717766 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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