• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Combined Relay Selection Enabled by Supervised Machine Learning

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Double (2).pdf
    Size:
    712.7Kb
    Format:
    PDF
    Description:
    Accepted manuscript
    Download
    Type
    Article
    Authors
    Dang, Shuping
    Tang, Jiashen
    Li, Jun
    Wen, Miaowen
    Abdullah, Salwani
    Li, Chengzhong
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2021-03-09
    Online Publication Date
    2021-03-09
    Print Publication Date
    2021-04
    Permanent link to this record
    http://hdl.handle.net/10754/668029
    
    Metadata
    Show full item record
    Abstract
    Combined relay selection only requires two relays to forward signals transmitted on multiple subcarriers, but the optimal outage performance is almost surely achievable in the high signal-to-noise ratio (SNR) region. However, because combined relay selection involves the generation of the full set of two-relay combinations, the selection complexity of combined relay selection is much higher than that of per-subcarrier relay selection when the number of relays goes large. This drawback restricts the implementation of combined relay selection in dense networks. To overcome this drawback, we propose to enable combined relay selection by supervised machine learning (ML). Because the training procedure is off-line, the proposed implementation scheme can considerably reduce the selection complexity and the processing latency. We carry out extensive experiments on TensorFlow 2.1 over a graphics processing unit (GPU) aided computing cloud server to validate the effectiveness of the proposed scheme. The experimental results confirm that supervised ML can provide near-optimal performance with lower computing latency that well matches that provided by brute-force search and the optimal relay selection in a per-subcarrier manner.
    Citation
    Dang, S., Tang, J., Li, J., Wen, M., Abdullah, S., & Li, C. (2021). Combined Relay Selection Enabled by Supervised Machine Learning. IEEE Transactions on Vehicular Technology, 1–1. doi:10.1109/tvt.2021.3065074
    Sponsors
    This work was supported in part by National Natural Science Foundation of China under Grant 61872102, in part by Guangxi Natural Science Foundation under Grant AD19245043, in part by Nanning Excellent Young Scientist Program under Grant RC20190201, in part by Guangxi Beibu Gulf Economic Zone Major Talent Program, in part by the International Collaborative Research Program of Guangdong Science and Technology Department under Grant No.2020A0505100061, in part by the Pearl River Nova Program of Guangzhou under Grant 201806010171, and in part by the Fundamental Research Funds for the Central Universities under Grant 2019SJ02.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Vehicular Technology
    DOI
    10.1109/TVT.2021.3065074
    Additional Links
    https://ieeexplore.ieee.org/document/9374099/
    https://ieeexplore.ieee.org/document/9374099/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9374099
    ae974a485f413a2113503eed53cd6c53
    10.1109/TVT.2021.3065074
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.