Dynamic eMBB scheduling strategy for GBR and NGBR in Non Standalone 5G NR: A deep reinforcement learning approach

Abstract

With the emergence of 5G networks and network slicing concept, efficient resource management is crucial to meet varied Quality of Service (QoS) requirements. Intra-slice scheduling plays a central role in optimizing the network performance while catering to the requirements of different traffic flows within a slice. In this paper, we propose a Deep Reinforcement Learning (DRL)-based scheduling scheme for eMBB applications. This appraoch aims to maximize system throughput, increase GBR throughput, minimize packet loss by minimizing Head of Line (HoL) delay, and ensure NGBR flow fairness. To evaluate our approach, we test and contrast the two DRL methods, i.e., Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). Using both approaches to fine-tune our hybrid scheduling metric, we demonstrate the adaptability and reliability of our approach with different learning frameworks. We contrast our DRL-based scheduler performance to the Proportional Fair (PF) scheduler and two QoS-aware schedulers, QoS and EXP-PF. Simulation shows that our scheme significantly improves the system throughput and maintains the GBR and NGBR traffic performance in balance. Moreover, the comparison of DQN and PPO provides novel insights into wireless scheduling efficacy with a foundation for future adaptive scheduling solutions in 5G and beyond.