A gNB-Driven Uplink Joint Time--Frequency Resource Allocation Scheme for IIoT-Oriented 5G-TSN Integrated Networks

With the rapid evolution of the Industrial Internet of Things (IIoT), industrial networks are required to support massive industrial devices with bounded low-latency transmission. To address these requirements, integrating the fifth-generation (5G) with time-sensitive networking (TSN) has been proposed. However, existing joint resource allocation methods struggle to achieve seamless low-latency deterministic scheduling between the 5G system (5GS) and TSN networks. Focusing on large-scale uplink transmission scenarios, a base station (gNB)-driven joint time-frequency resource allocation architecture is proposed. Within this architecture, the 5GS is modeled as a TSN bridge seamlessly integrated with the TSN cyclic queuing and forwarding (CQF) mechanism. Existing joint resource allocation algorithms suffer from local optima, low computational efficiency, and inability to capture global time-triggered (TT) flow interactions for globally optimal solutions. Accordingly, a gNB-driven multi-agent proximal policy optimization (MAPPO)-based joint time-frequency resource allocation algorithm, termed gNB-DMJRA, is further proposed. This algorithm adopts the centralized training and distributed execution (CTDE) framework, leveraging global information to better coordinate the scheduling of multiple TT flows and avoid convergence to local optimal solutions. In addition, the periodicity of TT flows is exploited to reduce computational complexity and the action space, thereby further improving learning efficiency and convergence. Simulation results demonstrate that under 1000 TT flows, the proposed algorithm reduces maximum latency by up to 74.27%, improves the scheduling success rate by 82.15%-331.75%, and achieves faster convergence than benchmarks, confirming its effectiveness and efficiency for large-scale TT flow scheduling.