Traffic Prediction-Assisted Resource Allocation for RAN Slicing Enabling URLLC and eMBB Coexistence

Abstract

The coexistence of Ultra-Reliable Low-Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB) can simultaneously meet the reliability and real-time performance requirements of critical services as well as the requirements of high-bandwidth services. In 5G and beyond 5G (B5G) networks, radio access network (RAN) slicing can ensure the differentiated quality of service (QoS) for coexisting heterogeneous services. This paper investigates a dynamic resource allocation framework, aiming to maximize resource utilization subject to QoS constraints. The optimization problem is an NP-hard integer program. To address this issue, we propose a hierarchical proximal policy optimization (PPO) model assisted by a traffic prediction algorithm, namely graph-aggregated Extended Long Short-Term Memory (GxLSTM), which decouples the resource allocation behavior between eMBB and URLLC. Transfer learning is introduced to improve the learning efficiency, forming a Transfer Learning-assisted Hierarchical PPO algorithm (TL-HPPO). In addition, considering the lightweight algorithm deployment requirements for edge networks, based on angular knowledge distillation (AKD), GxLSTM and TL-HPPO are respectively distilled to obtain AKD-traffic prediction (AKD-TP) and AKD-resource allocation (AKD-RA), thereby reducing computational complexity. Simulation results show that the proposed algorithms outperform comparative algorithms while ensuring QoS, effectively improving resource utilization.