In the paper, we explore the spectrum-data reconstruction of a spectrum-sensing system. In order to decease the demand on the sensed spectrum data, we proposed a deep convolutional neural network (DCNN) based spectrum data reconstruction scheme relying on three stages, thus the satellites are allowed to perform spectrum sensing with the aid of down-sampling, and transmit the low-resolution (LR) and small amount of high-resolution (HR) spectrum data to earth stations. Specifically, in the first stage, the received LR and HR spectrum data will be first preprocessed. Then, the preprocessed HR spectrum data will be sent into the DCNN model for training purposes in the second stage. In the third stage, the preprocessed LR spectrum data will be fed into the trained model with the aid of the optimized hyperparameters, and the trained DCNN can generate the HR spectrum data. Additionally, performance results show that the proposed reconstruction scheme can obtain the reconstructed HR spectrum data in terms of the low mean absolute error.


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    Title :

    Spectrum Data Reconstruction via Deep Convolutional Neural Network


    Additional title:

    Lect.Notes Social.Inform.


    Contributors:
    Wu, Qihui (editor) / Zhao, Kanglian (editor) / Ding, Xiaojin (editor) / Ding, Xiaojin (author) / Feng, Lijie (author)

    Conference:

    International Conference on Wireless and Satellite Systems ; 2020 ; Nanjing, China September 17, 2020 - September 18, 2020



    Publication date :

    2021-02-28


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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