Design of AoI-Aware 5G Uplink Scheduler UsingReinforcement Learning

TL;DR

This paper introduces an AoI-aware 5G uplink scheduler using reinforcement learning, specifically proximal policy optimization, to effectively balance AoI minimization and throughput in remote control environments.

Contribution

It proposes a novel reinforcement learning-based scheduler for 5G uplink that optimizes AoI and throughput trade-offs, demonstrating superior performance over baselines.

Findings

The RL-based scheduler reduces average AoI compared to baseline methods.

It maintains higher network throughput while minimizing AoI.

Simulation results validate the effectiveness of the proposed approach.

Abstract

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 showthat the proposed algorithm outperforms the considered baselines in terms of minimizing the expected AoI while maintaining the network throughput.