QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2107.06570v1
• Date: Wed, 14 Jul 2021 09:18:39 GMT
• Title: QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning
• Authors: Jakob Stigenberg, Vidit Saxena, Soma Tayamon, Euhanna Ghadimi
• Abstract summary: We propose a QA-Aware Deep Reinforcement learning Agent (QADRA) scheduler for NR networks. In our particular evaluation scenario, the QADRA scheduler improves network throughput by 30% while simultaneously maintaining the satisfaction rate of users served by the network.
• Score: 2.3857747529378917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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