发表机构
University of Málaga; Ericsson（马拉加大学; 爱立信）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于深度强化学习的RAN切片管理框架，用于在V2X与eMBB共存的5G网络中动态优化资源分配，实验证明其能平衡SLA合规与资源利用率，优于静态和比例分配策略。
AI 中文摘要
车联万物（V2X）通信的集成正在推动车载连接性的深刻变革，预计将显著提升交通效率和安全性。然而，V2X服务的严格要求，特别是超低延迟和高可靠性，带来了重大的技术挑战。5G的网络切片作为关键使能技术应运而生，通过提供定制的虚拟网络，确保异构服务的隔离性和适应性。本工作提出了一种智能无线接入网（RAN）切片管理框架，专门设计用于安全关键的V2X和高容量eMBB切片共存的场景。在这种复杂环境中，协调相互冲突的流量需求需要持续的数据驱动优化。为此，所提出的框架利用先进的深度强化学习（DRL）方法，实时动态优化资源分配。该框架在真实的5G独立组网（SA）网络上进行了实证验证，实验结果表明，DRL驱动的方法成功平衡了两个目标，通过最小化SLA违规同时确保eMBB切片的高资源利用率，优于传统的静态和比例分配策略。
英文摘要
The integration of Vehicle-to-Everything (V2X) communications is driving a profound transformation in vehicular connectivity, expected to significantly enhance traffic efficiency and safety. However, the stringent requirements of V2X services, particularly ultra-low latency and high reliability, present significant technical challenges. 5G's Network Slicing emerges as a key enabler by providing tailored virtual networks that ensure isolation and adaptability for heterogeneous services. This work proposes an intelligent Radio Access Network (RAN) slicing management framework specifically designed for scenarios where safety-critical V2X and high-capacity eMBB slices coexist. In such complex environments, harmonizing conflicting traffic requirements demands continuous, data-driven optimization. To achieve this, the proposed framework leverages an advanced Deep Reinforcement Learning (DRL) approach which dynamically optimizes resource allocation in real time. The framework is empirically validated on a real 5G Standalone (SA) network, where experimental results demonstrate that the DRL-driven approach successfully balances both objectives, outperforming traditional static and proportional allocation strategies by minimizing SLA violations while ensuring high resource utilization for eMBB slices.
