QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning

TL;DR

This paper introduces QADRA, a deep reinforcement learning-based scheduler for 5G NR networks that explicitly optimizes QoS satisfaction and network throughput, outperforming existing heuristics.

Contribution

The paper presents a novel deep reinforcement learning scheduler that jointly optimizes QoS satisfaction and network performance in 5G NR, trained end-to-end and evaluated in a realistic simulator.

Findings

QADRA improves network throughput by 30%.

QADRA maintains VoIP QoS satisfaction rate.

Significant performance boost over baseline schedulers.

Abstract

Fifth-generation (5G) New Radio (NR) cellular networks support a wide range of new services, many of which require an application-specific quality of service (QoS), e.g. in terms of a guaranteed minimum bit-rate or a maximum tolerable delay. Therefore, scheduling multiple parallel data flows, each serving a unique application instance, is bound to become an even more challenging task compared to the previous generations. Leveraging recent advances in deep reinforcement learning, in this paper, we propose a QoS-Aware Deep Reinforcement learning Agent (QADRA) scheduler for NR networks. In contrast to state-of-the-art scheduling heuristics, the QADRA scheduler explicitly optimizes for the QoS satisfaction rate while simultaneously maximizing the network performance. Moreover, we train our algorithm end-to-end on these objectives. We evaluate QADRA in a full scale, near-product, system level NR simulator and demonstrate a significant boost in network performance. In our particular evaluation scenario, the QADRA scheduler improves network throughput by 30% while simultaneously maintaining the QoS satisfaction rate of VoIP users served by the network, compared to state-of-the-art baselines.