• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • 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

    Compressive Sensing Based Grant-Free Random Access for Massive MTC

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Conference Paperfile1.pdf
    Size:
    227.6Kb
    Format:
    PDF
    Description:
    Post-print
    Download
    Type
    Conference Paper
    Authors
    Mei, Yikun
    Gao, Zhen
    Mi, De
    Xiao, Pei
    Alouini, Mohamed-Slim cc
    KAUST Department
    Communication Theory Lab
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2020-09-29
    Online Publication Date
    2020-09-29
    Print Publication Date
    2020-08
    Permanent link to this record
    http://hdl.handle.net/10754/665810
    
    Metadata
    Show full item record
    Abstract
    Massive machine-Type communications (mMTC) are expected to be one of the most primary scenarios in the next-generation wireless communications and provide massive connectivity for Internet of Things (IoT). To meet the demanding technical requirements for mMTC, random access scheme with efficient joint activity and data detection (JADD) is vital. In this paper, we propose a compressive sensing (CS)-based grant-free random access scheme for mMTC, where JADD is formulated as a multiple measurement vectors (MMV) CS problem. By leveraging the prior knowledge of the discrete constellation symbols, we develop an orthogonal approximate message passing (OAMP)-MMV algorithm for JADD, where the structured sparsity is fully exploited for enhanced performance. Moreover, expectation maximization (EM) algorithm is employed to learn the unknown sparsity ratio of the a priori distribution and the noise variance. Simulation results show that the proposed scheme achieves superior performance over other state-of-The-Art CS schemes.
    Citation
    Mei, Y., Gao, Z., Mi, D., Xiao, P., & Alouini, M.-S. (2020). Compressive Sensing Based Grant-Free Random Access for Massive MTC. 2020 International Conference on UK-China Emerging Technologies (UCET). doi:10.1109/ucet51115.2020.9205389
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2020 International Conference on UK-China Emerging Technologies, UCET 2020
    ISBN
    9781728194882
    DOI
    10.1109/UCET51115.2020.9205389
    Additional Links
    https://ieeexplore.ieee.org/document/9205389/
    http://epubs.surrey.ac.uk/858548/1/Compressive%20Sensing%20Based%20Grant-Free%20Random%20Access%20for%20Massive%20MTC%20-%20AAM.pdf
    ae974a485f413a2113503eed53cd6c53
    10.1109/UCET51115.2020.9205389
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Communication Theory Lab; 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.