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 The workshop invites submissions of the unpublished works on the following topics (but not limited to): Artificial Intelligence Aided Resource Allocation Deep Learning in Communication Networks and Coding Deep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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