Special Issue on Machine Learning in Wireless Networks

Submission Deadline: Mar. 20, 2020

Please click the link to know more about Manuscript Preparation: http://www.ijwcmc.org/submission

Please download to know all details of the Special Issue

Special Issue Flyer (PDF)
  • Lead Guest Editor
    • S M Shahrear Tanzil
      Ericsson R&D, Stockholm, Sweden
  • Guest Editor
    Guest Editors play a significant role in a special issue. They maintain the quality of published research and enhance the special issue’s impact. If you would like to be a Guest Editor or recommend a colleague as a Guest Editor of this special issue, please Click here to complete the Guest Editor application.
    • Nandinee Haq
      University of British Columbia, Vancouver, Canada
    • Ashim Biswas
      Ericsson R&D, Stockholm, Sweden
    • Sathishkumar Karupusamy
      Bharathiar University, Gobi, Tamilnadu, India
    • Praveena Venkatesan
      Anna University, Coimbatore, Tamilnadu, India
    • Chinnasamy P
      Anna University, Chennai, Tamil Nadu, India
    • Ahthasham Sajid
      Balochistan University of Information Technology Engineering and Management Sciences Quetta, Quetta, Balochistan, Pakistan
    • Devarani Devi Ningombam
      Department of Computer Engineering, Chosun University, Gwangju, South Korea
    • Prakasam Periasamy
      School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India
    • Rahul Paropkari
      Department of Computer Science and Electrical Engineering, University of Missouri Kansas City, Overland Park, Kansas, USA
    • G.S. Karthick
      Deparment of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India
  • Introduction

    Machine learning has captured great attention in recent years due to its critical problem-solving capability. On the other hands, the 5G network with its great speed, flexibility, and sophisticated design, makes machine learning an attractive way to solve challenging research problems. In this special issue, we are calling for papers that study how machine learning can be used to solve challenging research problem in wireless networks. The special issue will accept a wide range of research problems in the wireless network. A list of the topics covered by the special issue (but not limited to) is as follows.
    1. Machine learning for massive MIMO and beamforming
    2. Interference management in beamforming
    3. Resources management (MAC and transport layers) in wireless networks using reinforcement learning
    4. Edge computing and caching, content popularity prediction using transfer learning, deep neural network
    5. Mobility management, user activity pattern, traffic pattern in the network
    6. Any experimental/field trial results for new path loss models
    7. Heterogeneous network and self-organizing network
    8. Software-defined network and dynamic routing using machine learning
    9. Distributed and cloud radio access network
    10. Power amplifier efficient modulation schemes
    11. Traffic and task management in the datacenter
    12. Congestion control via user activity prediction

    Aims and Scope:

    1. Machine learning
    2. 5G communication
    3. Edge computing and caching, content popularity prediction, cloud RAN
    4. Massive MIMO/Beamforming
    5. Resource and mobility management in wireless networks
    6. Field trial in mm Wave

  • Guidelines for Submission

    Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.

    Papers should be formatted according to the guidelines for authors (see: http://www.ijwcmc.org/submission). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.