A 5G-TSN joint resource scheduling algorithm based on optimized deep reinforcement learning model for industrial networks

Abstract

As the Industrial Internet of Things (IIoT) evolves, the rapid growth of connected devices in industrial networks generates massive amounts of data. These transmissions impose stringent requirements on network communications, including reliable bounded latency and high throughput. To address these challenges, the integration of the fifth-generation (5G) mobile cellular networks and Time-Sensitive Networking (TSN) has emerged as a prominent solution for scheduling diverse traffic flows. While Deep Reinforcement Learning (DRL) algorithms have been widely employed to tackle scheduling issues within the 5G-TSN architecture, existing approaches often neglect throughput optimization in multi-user scenarios and the impact of Channel Quality Indicators (CQI) on resource allocation. To overcome these limitations, this study introduces ME-DDPG, a novel joint resource scheduling algorithm. ME-DDPG extends the Deep Deterministic Policy Gradient (DDPG) model by embedding a Modulation and Coding Scheme (MCS)-based priority scheme. This improvement in computational efficiency is critical for real-time scheduling in IIoT environments. Specifically, ME-DDPG provides latency guarantees for time-triggered applications, ensures throughput for video applications, and maximizes overall system throughput across 5 G and TSN domains. Simulation results demonstrate that the proposed ME-DDPG achieves 100 % latency reliability for time-triggered flows and improves system throughput by 10.84 % over existing algorithms under varying Gate Control List (GCL) configurations and user ratios. Furthermore, due to the combination of MCS-based resource allocation scheme with DDPG model, the proposed ME-DDPG achieves faster convergence speed of the reward function compared to the original DDPG method.