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 Submissions must be previously unpublished and may not be under consideration elsewhere.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.sciencepublishinggroup.com/journal/guideforauthors?journalid=133). 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.
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.
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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.
Machine learning for massive MIMO and beamforming
Interference management in beamforming
Resources management (MAC and transport layers) in wireless networks using reinforcement learning
Edge computing and caching, content popularity prediction using transfer learning, deep neural network
Mobility management, user activity pattern, traffic pattern in the network
Any experimental/field trial results for new path loss models
Heterogeneous network and self-organizing network
Software-defined network and dynamic routing using machine learning
Distributed and cloud radio access network
Power amplifier efficient modulation schemes
Traffic and task management in the datacenter
Congestion control via user activity prediction
Aims and Scope:
Edge computing and caching, content popularity prediction, cloud RAN
Resource and mobility management in wireless networks