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Machine learning is among the most active research fields today, and its successful applications to wireless communication networks is highly expected, both by the academia and the industry. Yet, transforming this expectation into reality requires significant research efforts, an ongoing discussion among involved researchers from the communications and machine learning communities, and the overcoming of several obstacles. Hence, this workshop will aim at providing a forum for researchers to discuss pioneering works that have successfully applied machine learning to problems in communication networks, the discussion of challenges that need to be overcome, and the understanding of the potential achievements that can be expected. Finally, contributions to the field of machine learning building on existing methods from the fields of communications, signal processing, and information and coding theory will also be discussed, in a true crossfertilization spirit.
The workshop invites submissions of the unpublished works on the following topics (but not limited to):
Artificial Intelligence Aided Resource Allocation
Deep Learning in Communication Networks and Coding
Deep learning Enabled Edge Caching and Computing
Distributed Machine Learning
Generative Adversarial Networks for Communications
Intelligent Agents for Mobile Edge Computing (MEC)
Intelligent Energy-aware/Green Communications
Intelligent Software Defined Networks
Machine Learning for 5G Communication Networks
Machine Learning for Integrated Networking, Caching and Computing
Machine learning for Information Centric Networking
Machine Learning for Localization and UAVs
Machine Learning for Network Slicing Optimization
Machine Learning for Security and Detection
Multi-Agent Learning for Communications
Reinforcement Learning for Communications
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