A comprehensive review of the theory, application, and research of machine learning for future wireless communications

Successful businesses are looking to scale their speed of business with Machine Learning (ML). From engineering to operations, ML has proven to increase efficiency and boost profits. Yet for many organizations, ML initiatives are exclusively driven by small development teams with limited, priority-based resources. Attend this session to learn why you don’t have to rely on experts to leverage the power of ML. Step through a few ML use cases in telecom, learn some tips and techniques to overcome formidable obstacles, as well as some common tools to get started.

Machine Learning for Future Wireless Communications provides comprehensive and highly accessible treatment to the theory, applications, and current research developments to the technical aspects related to machine learning for wireless communications and networks. The technology development of ML for wireless communications has grown explosively and is one of the biggest trends in related academic, research, and industry communities.

Deep neural network-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience, and silicon convergence.

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