• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Computer Science & Engineering
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Computer Science & Engineering
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Joint resource allocation and power control for D2D communication with deep reinforcement learning in MCC

    Thumbnail
    View/Open
    Publisher version (You have accessOpen AccessIcon)
    Publisher version (Check access options)
    Check access options
    Joint resource allocation and power control for D2D communication with deep reinforcement learning in MCC.pdf (1.816Mb)
    Date
    2021-04-01
    Author
    Wang, Dan
    Qin, Hao
    Song, Bin
    Xu, Ke
    Du, Xiaojiang
    Guizani, Mohsen
    ...show more authors ...show less authors
    Metadata
    Show full item record
    Abstract
    Mission-critical communication (MCC) is one of the main goals in 5G, which can leverage multiple device-to-device (D2D) connections to enhance reliability for mission-critical communication. In MCC, D2D users can reuses the non-orthogonal wireless resources of cellular users without a base station (BS). Meanwhile, the D2D users will generate co-channel interference to cellular users and hence affect their quality-of-service (QoS). To comprehensively improve the user experience, we proposed a novel approach, which embraces resource allocation and power control along with Deep Reinforcement Learning (DRL). In this paper, multiple procedures are carefully designed to assist in developing our proposal. As a starter, a scenario with multiple D2D pairs and cellular users in a cell will be modeled; followed by the analysis of issues pertaining to resource allocation and power control as well as the formulation of our optimization goal; and finally, a DRL method based on spectrum allocation strategy will be created, which can ensure D2D users to obtain the sufficient resource for their QoS improvement. With the resource data provided, which D2D users capture by interacting with surroundings, the DRL method can help the D2D users autonomously selecting an available channel and power to maximize system capacity and spectrum efficiency while minimizing interference to cellular users. Experimental results show that our learning method performs well to improve resource allocation and power control significantly.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098740731&origin=inward
    DOI/handle
    http://dx.doi.org/10.1016/j.phycom.2020.101262
    http://hdl.handle.net/10576/35868
    Collections
    • Computer Science & Engineering [‎2429‎ items ]

    entitlement


    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission

    Help

    Item Submission Publisher policiesUser guides FAQs

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video