Abstract: In this talk, we present algorithms for radio resource management (RRM) in ultra-dense wireless networks, where a group of transmit points (TPs) intend to serve multiple user equipment devices (UEs) using the same wireless resource. We start with a centralized RRM algorithm, which is derived based on the information-theoretic optimality condition for treating interference as noise. We then introduce a scalable distributed RRM approach using multi-agent deep reinforcement learning (RL). We equip each TP in the network with a deep RL agent, which receives partial delayed observations from its own associated UEs, while also exchanging observations with its neighboring agents. Based on these observations, each TP decides on which user to serve and what transmit power level to use at each scheduling interval. We finally discuss how graph neural network (GNN) architectures can be leveraged to exploit the underlying network topology in order to learn power control policies in an unsupervised manner.

Bio: Navid Naderi is a Research Scientist in the Information & Systems Sciences Lab at HRL Laboratories in Malibu, CA. Prior to that, he was a Research Scientist at Intel Labs in Santa Clara, CA. He received his PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA in 2016 and my MSc in Electrical and Computer Engineering from Cornell University, Ithaca, NY in 2014, both under Prof. Salman Avestimehr. His research interests include development and analysis of model-based and learning-based radio resource allocation algorithms for 5G and beyond. Dr. Naderializadeh ranked first in the Iranian Nationwide University entrance exam in 2007. He was the recipient of Jacobs Scholarship in 2011. He was selected as a 2015-16 Ming Hsieh Institute Ph.D. Scholar. He has also been a finalist in the Shannon Centennial Student Competition at Nokia Bell Labs in 2016.