Machine Learning in Wireless Networks
Submission Deadline: Dec. 20, 2019

This special issue currently is open for paper submission and guest editor application.

Join as Guest Editor Submit to Special Issue
Lead Guest Editor
S M Shahrear Tanzil
Ericsson R&D, Stockholm, Sweden
Guest Editors
  • Nandinee Haq
    University of British Columbia, Vancouver, Canada
  • Ashim Biswas
    Ericsson R&D, Stockholm, Sweden
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 Submissions must be previously unpublished and may not be under consideration elsewhere.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: 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 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.
Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.

Special Issue Flyer (PDF)

Please download to know all details of the
Special Issue

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
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