Reinforcement Learning-Based Genetic Algorithm for Differentiated Traffic Scheduling in Industrial TSN-5G Networks

In order to ensure reliable transmission of important traffic in industrial networks, time-sensitive network (TSN) technology and fifth-generation mobile communication technology (5G) are introduced into the industrial network. However, there are still challenges in integrating TSN networks with 5G networks, especially in terms of end-to-end scheduling in hybrid systems. Considering the diverse range of traffic types and their end-to-end transmission requirements within the industrial Internet, we propose a differentiated traffic scheduling model and develop a population generation algorithm, termed Genetic Algorithm (GA) based two-stage population generation algorithm (PTPG). Notably, the algorithm utilizes a non-target training approach to generate the initial population and integrate Proximal Policy Optimization (PPO) to improve algorithm convergence and facilitate the inheritance of advantages across generations. The simulation results demonstrate notable enhancements in end-to-end delay, the number of occupied queues, and algorithm convergence status compared to other algorithms.