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

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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.
Titel2021 IEEE 4th 5G World Forum (5GWF)
Antal sider6
Publikationsdato15 okt. 2021
ISBN (Trykt)978-1-6654-4309-8
ISBN (Elektronisk)978-1-6654-4308-1
StatusUdgivet - 15 okt. 2021
Begivenhed2021 IEEE 4th 5G World Forum (5GWF) - Montreal, Canada
Varighed: 13 okt. 202115 okt. 2021


Konference2021 IEEE 4th 5G World Forum (5GWF)


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