IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i11p342-d979536.html
   My bibliography  Save this article

Editorial for the Special Issue on 5G Enabling Technologies and Wireless Networking

Author

Listed:
  • Michael Mackay

    (School of Computer Science and Mathematics, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK)

Abstract

The ongoing deployment of 5G networks is seen as a key enabler for realizing upcoming interconnected services at scale, including the massive deployment of the Internet of Things, providing V2X communications to support autonomous vehicles, and the increase in smart homes, smart cities, and Industry 4 [...]

Suggested Citation

  • Michael Mackay, 2022. "Editorial for the Special Issue on 5G Enabling Technologies and Wireless Networking," Future Internet, MDPI, vol. 14(11), pages 1-2, November.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:11:p:342-:d:979536
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/11/342/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/11/342/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lopamudra Hota & Biraja Prasad Nayak & Arun Kumar & G. G. Md. Nawaz Ali & Peter Han Joo Chong, 2021. "An Analysis on Contemporary MAC Layer Protocols in Vehicular Networks: State-of-the-Art and Future Directions," Future Internet, MDPI, vol. 13(11), pages 1-45, November.
    2. Lorenzo Ricciardi Celsi & Andrea Caliciotti & Matteo D'Onorio & Eugenio Scocchi & Nour Alhuda Sulieman & Massimo Villari, 2021. "On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks," Future Internet, MDPI, vol. 13(10), pages 1-16, October.
    3. Jesús Fernando Cevallos Moreno & Rebecca Sattler & Raúl P. Caulier Cisterna & Lorenzo Ricciardi Celsi & Aminael Sánchez Rodríguez & Massimo Mecella, 2021. "Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach," Future Internet, MDPI, vol. 13(11), pages 1-28, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Keywords

      n/a;

      Statistics

      Access and download statistics

      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:14:y:2022:i:11:p:342-:d:979536. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.