Dr. Arumugam Nallanathan on April 20th 17 Hs USA EDT

(22 hrs London / 17 hrs Chi / 18 hrs Arg Bra Uru / 16 hrs Ecu Per Col Mex / 15 hrs Costa Rica).

dr arumugam nallanathanDr. Arumugam Nallanathan, FIET, Fellow IEEE, Professor of Wireless Communications, Head of Communication Systems Research (CSR) Group, School of Electronic Engineering and Computer Science, Faculty of Science and Engineering, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom,

His research interests include Artificial Intelligence for Wireless Systems, 5G and beyond Wireless Networks, Internet of Things (IoT) and Molecular Communications. He published nearly 500 technical papers (including more than 200 top IEEE journal papers) in scientific journals and international conferences. He is a co -recipient of the Best Paper Awards presented at the IEEE International Conference on Communications 2016 (ICC’2016), IEEE Global Communications Conference 2017 (GLOBECOM’2017) and IEEE Vehicular Technology Conference 2017 (VTC’2017). He is an Editor for IEEE Transactions on Communications and a Senior Editor for IEEE Wireless Communications Letters. He was an Editor for IEEE Transactions on Wireless Communications (2006-2011), IEEE Transactions on Vehicular Technology (2006-2017) and IEEE Signal Processing Letters. He served as the Chair for the Signal Processing and Communication Electronics Technical Committee of IEEE Communications Society and Technical Program Chair and member of Technical Program Committees in numerous IEEE conferences. He received the IEEE
Communications Society SPCE outstanding service award 2012 and IEEE Communications Society RCC outstanding service award 2014. He has been selected as a Web of Science (ISI) Highly Cited Researcher in 2016. He is an IEEE Fellow and IEEE Distinguished Lecturer.

LECTURE TOPICS: Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems. However, their Random Access CHannel (RACH) procedure suffers from unreliability, due to the collision from the simultaneous massive access. Despite the fact that this collision problem has been treated in existing RACH schemes, these schemes usually organize IoT devices’ transmission and re-transmission along with fixed parameters, thus can hardly adapt to time-varying traffic patterns. Without adaptation, the RACH procedure easily suffers from high access delay, high energy consumption, or even access unavailability. In this talk, how to optimize the RACH procedure in real-time by maximizing a long-term hybrid multi-objective function, which consists of the number of access success devices, the average energy consumption, and the average access delay will be presented.

AGENDA: Introduction to NB-IoT, Challenges in NB-IoT, Solution using Deep Reinforcement Learning

REGISTRATION: https://events.vtools.ieee.org/event/register/269122