Design of AoI-Aware 5G Uplink Scheduler Using Reinforcement Learning

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review


Age of Information (AoI) reflects the time that is elapsed from the generation of a packet by a 5G user equipment (UE) to the reception of the packet by a controller. A design of an AoI-aware radio resource scheduler for UEs via reinforcement learning is proposed in this paper. In this paper, we consider a remote control environment in which a number of UEs are transmitting time-sensitive measurements to a remote controller. We consider the AoI minimization problem and formulate the problem as a trade-off between minimizing the sum of the expected AoI of all UEs and maximizing the throughput of the network. Inspired by the success of machine learning in solving large networking problems at low complexity, we develop a reinforcement learning-based method to solve the formulated problem. We used the state-of-the-art proximal policy optimization algorithm to solve this problem. Our simulation results show that the proposed algorithm outperforms the considered baselines in terms of minimizing the expected AoI while maintaining the network throughput.
Original languageEnglish
Title of host publication2021 IEEE 4th 5G World Forum (5GWF)
Number of pages6
Publication date15 Oct 2021
Article number9604981
ISBN (Print)978-1-6654-4309-8
ISBN (Electronic)978-1-6654-4308-1
Publication statusPublished - 15 Oct 2021
Event2021 IEEE 4th 5G World Forum (5GWF) - Montreal, Canada
Duration: 13 Oct 202115 Oct 2021


Conference2021 IEEE 4th 5G World Forum (5GWF)


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