Deep Learning Meets Cognitive Radio: Predicting Future Steps

SHENFIELD, Alex, KHAN, Zaheer and AHMADI, Hamed (2020). Deep Learning Meets Cognitive Radio: Predicting Future Steps. In: 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). IEEE.

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Official URL: https://ieeexplore.ieee.org/document/9129042
Link to published version:: https://doi.org/10.1109/VTC2020-Spring48590.2020.9129042
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    Abstract

    Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations. In this work we proposed a Deep Learning (DL) approach to learn the channel occupancy model and predict its availability in the next time slots. Our results show that the proposed DL approach outperforms existing works by 5%. We also show that our proposed DL approach predicts the availability of channels accurately for more than one time slot.

    Item Type: Book Section
    Additional Information: © 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works ISSN: 2577-2465 25-28 May 2020 Antwerp, Belgium
    Identification Number: https://doi.org/10.1109/VTC2020-Spring48590.2020.9129042
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 11 Mar 2020 10:21
    Last Modified: 20 Jul 2020 19:45
    URI: http://shura.shu.ac.uk/id/eprint/25962

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