Leveraging Reinforcement Learning for a Novel Traffic-Aware Scheduler in 5G NR IIoT Networks

The integration of 5G New Radio (NR) into the manufacturing industry stands as a pivotal aspect of the Industrial Internet of Things (IIoT) paradigm, mitigating constraints posed by traditional wired communication technologies in terms of flexibility, mobility, and adaptability. However, ensuring comparable performance levels presents challenges in the scheduling design of 5G NR networks, particularly due to the emphasis on uplink transmissions within industrial applications. Indeed, current uplink schedulers proposed by the 3rd Generation Partnership Project (3GPP) exhibit performance trade-offs, forcing the network to acquire knowledge of uplink traffic and faltering in the presence of heterogeneous traffic sources, commonplace in industrial environments. In this paper, we thus propose a Reinforcement Learning (RL)-based approach that dynamically assigns users to the most suitable uplink scheduler without prior or acquired knowledge of the different traffic patterns. Through 3GPP-compliant network simulations, we demonstrate the effectiveness of our approach in optimizing network performance while addressing the complexities of heterogeneous IIoT environments and maintaining standard compliance.