IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v73y2020i1d10.1007_s11235-019-00598-0.html
   My bibliography  Save this article

Energy and spectral efficiency optimization using probabilistic based spectrum slicing (PBSS) in different zones of 5G wireless communication network

Author

Listed:
  • Amika Pal Sundan

    (Shri Mata Vaishno Devi University)

  • Rakesh Kumar Jha

    (Shri Mata Vaishno Devi University)

  • Akhil Gupta

    (Lovely Professional University)

Abstract

Spectrum Slicing is arising as an important notion for 5G wireless network as it helps in increasing the data rate, capacity and therefore energy efficiency and spectral efficiency of 5G network. In this paper, traffic modelling is done on the basis of user density and demand. The system model for spectrum slicing is analyzed on the basis of traffic density pattern analysis so that utilization of spectrum are based on probability of active users in different zones i.e. urban, sub-urban and rural area which has the objective of increasing spectral efficiency. Moreover, Hidden Markov Model is used for training and preserving of Base station such that probabilistic spectrum allocation to different user densities can be achieved which aims to use the spectrum efficiently. Novel spectrum slicing technique can contribute a platform for people belonging to Below Poverty Line such that they can make use of spectrum freely. This approach not only reduce the wastage of spectrum but also reduces the interference and hence enhances the spectral efficiency and energy efficiency which optimizes the power so that high QoE and QoS can be achieved.

Suggested Citation

  • Amika Pal Sundan & Rakesh Kumar Jha & Akhil Gupta, 2020. "Energy and spectral efficiency optimization using probabilistic based spectrum slicing (PBSS) in different zones of 5G wireless communication network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 73(1), pages 59-73, January.
  • Handle: RePEc:spr:telsys:v:73:y:2020:i:1:d:10.1007_s11235-019-00598-0
    DOI: 10.1007/s11235-019-00598-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-019-00598-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11235-019-00598-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:telsys:v:73:y:2020:i:1:d:10.1007_s11235-019-00598-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.