IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v12y2020i6p99-d368861.html
   My bibliography  Save this article

Performance Model for Video Service in 5G Networks

Author

Listed:
  • Jiao Wang

    (Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01854, USA)

  • Jay Weitzen

    (Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01854, USA)

  • Oguz Bayat

    (Graduate School of Science and Engineering, Altinbas University, 34217 Istanbul, Turkey)

  • Volkan Sevindik

    (Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01854, USA)

  • Mingzhe Li

    (Q Factor Communications, 255 Bear Hill Road, Waltham, MA 02451, USA)

Abstract

Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.

Suggested Citation

  • Jiao Wang & Jay Weitzen & Oguz Bayat & Volkan Sevindik & Mingzhe Li, 2020. "Performance Model for Video Service in 5G Networks," Future Internet, MDPI, vol. 12(6), pages 1-21, June.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:6:p:99-:d:368861
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/12/6/99/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/12/6/99/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:12:y:2020:i:6:p:99-:d:368861. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.