一种基于深度强化学习的5G网络中V2X服务等级协议合规的RAN切片资源划分优化
A DRL-Driven Optimization of RAN Slice Resource Partitioning for V2X SLA Compliance in 5G Networks
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中文总结 AI 辅助
针对5G网络中V2X服务的高时延与可靠性要求，提出一种基于PPO的深度强化学习方法来优化RAN切片的PRB资源划分，在满足V2X服务等级协议的同时提升资源利用率并减少对eMBB服务的影响。
中文摘要 AI 辅助
车联万物（V2X）通信在时延和可靠性方面提出了非常严格的要求，这些要求必须在具有不同性能目标的多种服务共存的场景中得到满足。在这种场景下，高流量密集型服务竞争有限的无线资源，使得满足V2X服务需求变得复杂。在此背景下，网络切片（NS）作为关键因素应运而生，它能够创建多个切片并在它们之间分配资源，以满足异构服务需求。具体而言，本工作从高流量需求条件下的物理资源块（PRB）划分角度，研究了无线接入网（RAN）切片问题。为此，提出了一种基于近端策略优化（PPO）的强化学习方法，以确定满足V2X服务严格时延和可靠性要求的PRB分配，同时提高资源利用效率并最小化增强移动宽带（eMBB）服务的性能退化。所提出的解决方案通过在不同流量负载和不同V2X服务需求下的仿真实验进行评估，证明了其能够根据网络条件和服务需求调整资源划分的能力。
英文摘要
Vehicle-to-Everything (V2X) communications impose very demanding requirements in terms of latency and reliability, which must be met in scenarios where multiple services with diverse performance targets coexist. In such scenarios, traffic-intensive services compete for limited radio resources, complicating the fulfillment of V2X service demands. Within this context, Network Slicing (NS) emerges as a key factor that enables the creation of multiple slices and the allocation of resources among them to satisfy heterogeneous service requirements. In particular, this work addresses the Radio Access Network (RAN) slicing problem from the perspective of Physical Resource Block (PRB) partitioning under high traffic demand conditions. To this end, a reinforcement learning approach based on Proximal Policy Optimization (PPO) is proposed to determine PRB allocations that satisfy the strict latency and reliability requirements of V2X services, while improving resource utilization efficiency and minimizing performance degradation of enhanced Mobile BroadBand (eMBB) services. The proposed solution is evaluated through simulation-based experiments under various traffic loads and different V2X service requirements, demonstrating its ability to adapt resource partitioning to network conditions and service demands.
发表机构
- University of Málaga（马拉加大学）
- Ericsson（爱立信）
机构由 AI 辅助整理，请以论文原文为准。
