Generalizable 5G RAN/MEC Slicing and Admission Control for Reliable Network Operation

Abstract

The virtualization and distribution of 5G Radio Access Network (RAN) functions across radio unit (RU), distributed unit (DU), and centralized unit (CU) in conjunction with multi-access edge computing (MEC) enable the creation of network slices tailored for various applications with distinct quality of service (QoS) demands. Nonetheless, given the dynamic nature of slice requests and limited network resources, optimizing long-term revenue for infrastructure providers (InPs) through real-time admission and embedding of slice requests poses a significant challenge. Prior works have employed Deep Reinforcement Learning (DRL) to address this issue, but these approaches require re-training with the slightest topology changes due to node/link failure or overlook the joint consideration of slice admission and embedding problems. This paper proposes a novel method, utilizing multi-agent DRL and Graph Attention Networks (GATs), to overcome these limitations. Specifically, we develop topology-independent admission and slicing agents that are scalable and generalizable across diverse metropolitan networks. Results demonstrate substantial revenue gains-up to 35.2% compared to heuristics and 19.5% when compared to other DRL-based methods. Moreover, our approach showcases robust performance in different network failure scenarios and substrate networks not seen during training without the need for re-training or re-tuning. Additionally, we bring interpretability by analyzing attention maps, which enables InPs to identify network bottlenecks, increase capacity at critical nodes, and gain a clear understanding of the model decision-making process.