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 architecture 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,control and execution traffic flow,and data analysis and business optimization flow,implementing cross domain end-to-end low latency 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 resources,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 successful scheduling of mixed traffic flows.