Hierarchical Reinforcement Learning Based Traffic Steering in Multi-RAT 5G Deployments (Best Paper Award)
Published in IEEE ICC23, 2023
Recommended citation: M. A. Habib, H. Zhou, P. E. Iturria-Rivera, M. Elsayed, M. Bavand, R. Gaigalas, S. Furr, and M. Erol-Kantarci, “Hierarchical Reinforcement Learning Based Traffic Steering in Multi-RAT 5G Deployments",May. 2023. https://arxiv.org/abs/2301.07818
In 5G non-standalone mode, an intelligent traffic steering mechanism can vastly aid in ensuring smooth user experience by selecting the best radio access technology (RAT) from a multi-RAT environment for a specific traffic flow. In this paper, we propose a novel load-aware traffic steering algorithm based on hierarchical reinforcement learning (HRL) while satisfying diverse QoS requirements of different traffic types. HRL can significantly increase system performance using a bi-level architecture having a meta-controller and a controller. In our proposed method, the meta-controller provides an appropriate threshold for load balancing, while the controller performs traffic admission to an appropriate RAT in the lower level. Simulation results show that HRL outperforms a Deep Q-Learning (DQN) and a threshold-based heuristic baseline with 8.49%, 12.52% higher average system throughput and 27.74%, 39.13% lower network delay, respectively.
Recommended citation: M. A. Habib, H. Zhou, P. E. Iturria-Rivera, M. Elsayed, M. Bavand, R. Gaigalas, S. Furr, and M. Erol-Kantarci, “Hierarchical Reinforcement Learning Based Traffic Steering in Multi-RAT 5G Deployments”,May. 2023.