Machine Learning in Wireless Networks
Submission DeadlineDec. 20, 2019

Submission Guidelines: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=133

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
  • 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
    Department of Electronics and Communication Engineering & Center for Learning and Teaching, Coimbatore, Tamil Nadu, India
  • Rahul Paropkari
    Department of Computer Science and Electrical Engineering, University of Missouri Kansas City, Overland Park, Kansas, USA
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 should be formatted according to the guidelines for authors
(see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=133).

Please download the template to format your manuscript.

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