Multicast Wireless Resource Optimization for High-Precision Clock Synchronization Timing Service in 5G-TSN

In the context of utilizing 5G wireless technology for facilitating the timing service of the industrial Internet of Things (IoT), achieving precise clock synchronization while considering the balance between 5G wireless resource utilization and network performance becomes imperative. Firstly, an incomplete observation clock synchronization model is established. Subsequently, the Kalman filter algorithm is employed to determine the boundedness of clock synchronization error, enabling the formulation of an optimization model for 5G wireless resource allocation aimed at ensuring clock synchronization accuracy. Furthermore, a two-step optimization framework is introduced, employing a clustering algorithm and Lyapunov method, combined with multi-agent deep reinforcement learning, to effectively address the proposed problem. Simulation results corroborate the efficacy and superiority of the proposed model and methodology in achieving a joint optimization of clock synchronization accuracy and throughput.