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University of Hawaii

Electrical Engineering

Learning Based Dynamic Optimization in Massive IoT Networks

Date: 2019-12-06           Add to Google Calendar
Time: 11:30am - 12:30pm
Location: POST 214
Speaker: Dr. Arumugam Nallanathan, Queen Mary University of London

Synopsis: Narrow Band-Internet of Things (NB-IoT) is an emerging cellular-based radio access technology, which offers a range of flexible configurations for different coverage enhancement (CE) groups to provide reliable uplink connections for massive IoT devices with diverse data traffic. To optimize the number of served IoT devices, the uplink resource configurations need to be adjusted in real-time according to the dynamic traffic, this brings the challenge of how to select the configurations at the Evolved Node B (eNB) in the multiple CE groups scenario with high-dimension and interdependency. To tackle this challenge, multi-agent reinforcement learning (RL) is proposed as a promising solution, where the RL agent (i.e., implemented at the eNB) automatically updates the uplink resource configuration by interacting with the environment. In this talk, Professor Nallanathan will explain how the machine learning techniques such as can be used dynamically to solve the numerous challenges in the Internet of Things (IoT).

Bio: Arumugam Nallanathan is Professor of Wireless Communications and the head of the Communication Systems Research (CSR) group in the School of Electronic Engineering and Computer Science at Queen Mary University of London since September 2017. He was with the Department of Informatics at King’s College London from December 2007 to August 2017, where he was Professor of Wireless Communications from April 2013 to August 2017 and a Visiting Professor from September 2017. He was an Assistant Professor in the Department of Electrical and Computer Engineering, National University of Singapore from August 2000 to December 2007. He has been selected as a Web of Science (ISI) Highly Cited Researcher in 2016. He is an IEEE Fellow and IEEE Distinguished Lecturer.



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