Network-Aided Intelligent Traffic Steering in 6G ORAN: A Multi-Layer Optimization Framework
Dr. Nguyen received the B.E. degree in electrical engineering from Ho Chi Minh City University of Technology, Vietnam, in 2012 and the M.E. and Ph.D. degrees in electronic engineering from Soongsil University, Seoul, South Korea, in 2015 and 2018, respectively. He is currently an Assistant Professor of Electrical Engineering at the College of Engineering and Computer Science, VinUniversity, Vietnam. Prior to this position, he was a Research Associate with the Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg (2019-2022), a Postdoc Researcher and a Lecturer with Soongsil University (2018-2019), a Postdoctoral Visiting Scholar with the University of Technology Sydney, AUS (July-August 2018) and a Ph.D. Visiting Scholar with Queen’s University Belfast, U.K. (June-July 2015 and August 2016). Dr. Dinh received the three best conference paper awards, IEEE Communications Letters Exemplary Editor Awards 2019 and 2021, IEEE Open Journal of the Communications Society Exemplary Editor Award 2021, IEEE Transaction on Communications Exemplary Reviewer Award 2018 and IEEE GLOBECOM Student Travel Grant Award 2017. He has authored or co-authored some 70 papers published in international journals and conference proceedings. He has served as a reviewer for many top-tier international journals on wireless communications, and has also been a Technical Program Committee Member for several flag-ship international conferences in the related fields. He is an Editor for the IEEE Open Journal of the Communications Society and IEEE Communications Letters.
To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (ORAN). So far, however, the applicability of ORAN in controlling and optimizing RAN functions remains rather limited. In this paper, we optimize a joint flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in ORAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: $i$) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; $ii$) we develop low-complexity algorithms based on the reinforcement learning, and inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and $iii)$ the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.
Related seminars
Stony Brook University
Washington State University
University of Melbourne
National University of Singapore