针对铁路运维场景中混合业务流的端到端传输,提出了数字孪生架构下基于深度强化学习的确定性传输调度机制,即在线混合业务流端到端传输调度机制(End-to-End Transmission Scheduling Mechanism for Online Mixed-traffic,E2ETSM-OMT).该机制基于差异化调度策略的思想,将业务流分为监控与数据采集流、控制与执行业务流和数据分析与业务优化流3类,通过确定性技术实现跨域端到端低时延传输.进一步地,通过模型映射和行为映射,将物理空间全方位、高精度地映射到虚拟空间,在数字孪生网络中构建混合业务的拓扑结构,预先分配数据传输路径和时隙资源,从而减少不同业务流之间的调度冲突和资源竞争.同时,通过深度强化学习(Deep Reinforcement Learning,DRL)智能体在线决策,兼顾效果与效率,对不同收益的业务流进行调度.与已有机制相比,数字孪生技术可以实现物理世界与虚拟世界的相互映射,实现非平稳通信环境下DRL的应用,避免在现实网络中探索造成的服务质量下降.仿真结果表明,所提出的面向数字孪生的确定性传输调度机制在保障成功调度混合业务流的同时,以较低的端到端整体时延实现了较高的传输收益.
Deterministic Transmission Scheduling Mechanism for Mixed Traffic Flows Towards Digital Twin Networks
A deterministic transmission scheduling mechanism based on deep reinforcement learning under the digital twin archi-tecture is proposed for the end-to-end transmission of mixed traffic flows in railway operation and maintenance scenarios,namely end-to-end transmission scheduling mechanism for online mixed-traffic(E2ETSM-OMT).Based on the idea of differentiated scheduling strategy,the proposed mechanism divides traffic flows into three categories:monitoring and data collection flow,con-trol and execution traffic flow,and data analysis and business optimization flow,implementing cross domain end-to-end low laten-cy transmission through deterministic technologies.Meanwhile,through model mapping and behavior mapping,the physical space is projected to the virtual space with high precision in all directions.In the digital twin network,after constructing the topology of mixed flows,deep reinforcement learning(DRL)agent makes pre-allocation decisions of transmission path and time slot re-sources,taking the effect and efficiency into account,so as to reduce scheduling conflicts and resource competition among different traffic flows.Compared with the existing mechanisms,digital twin technologies can realize the mutual mapping between the phy-sical world and the virtual world,realize the application of DRL in non-stationary communication environment,and avoid the loss of service quality caused by exploration in the real network.Simulation results show that the digital twin-oriented deterministic transmission scheduling mechanism achieves high transmission benefits with low end-to-end overall delay while ensuring success-ful scheduling of mixed traffic flows.
Digital twinDeterministic networkCross-domain transmissionDifferentiated schedulingDeep reinforcement learning
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