Machine Learning and Artificial Intelligence for Future Generation Communication Networks

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With the implementation of the fifth generation of mobile systems (5G) and the emergence of internet-of-things (IoT), the number of devices connected to the Internet is expected to grow tremendously over the next few decades. At the same time, machine learning (ML) and artificial intelligence (AI) are becoming an integral part of many applications that run on mobile devices. This raises two interesting questions. First, with the complexity of the modern-day communication networks, how can ML and AI be leveraged to improve the design of these systems? And second, how can communication networks help improve distributed ML and AI? This research program develops cutting edge technologies for IoT using ML and AI in three key areas: a) how ML and AI can help improve localization and authentication at the physical layer; b) how ML and AI can be used to detect chemical sources in complex networks of vessels; and c) how deep neural networks can be distributed and trained over IoT devices, edge nodes, and cloud servers in the present of communication links that introduce errors and delay. The ML- and AI-based technologies arising from this proposed research are expected to significantly improve the speed, performance and dynamic adaptability of wireless networks, thereby enabling enhanced communications among IoT devices and infrastructure within multiple sectors. This will have a profound impact in smart cities, smart health monitoring, and other areas where distributed sensing, localization, authentication, and deep learning are required (e.g. manufacturing, robotics and autonomous vehicles).