Joint Resource Allocation to Transparently Integrate 5G TDD Uplink with Time-Aware TSN

2025-11-28

AI Summary

Ensuring deterministic uplink performance for time-sensitive traffic in industrial 5G–Time-Sensitive Networking (TSN) convergence under mobility remains challenging. Method: This paper proposes a heterogeneous wireless resource joint-scheduling architecture, pioneering the use of 5G TDD base stations as transparent TSN bridges to enable end-to-end time-aware scheduling. It integrates static configuration with dynamic scheduling (Proportional Fair/Max C/I), jointly modeling bridging latency, time-aware traffic shaping, and flow filtering & policing to guarantee deadline compliance for periodic flows. Contribution/Results: Experiments demonstrate a 28% improvement in radio resource efficiency over Configured Grant baseline; 100% of time-sensitive flows meet their deadlines; and non-deterministic traffic throughput increases significantly. The work establishes, for the first time, the feasibility of deploying 5G infrastructure as transparent TSN bridges and delivers a verifiable, deterministic uplink scheduling framework for industrial 5G–TSN integration.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

To enable mobility in industrial communication systems, the seamless integration of 5G with Time-Sensitive Networking (TSN) is a promising approach. Deterministic communication across heterogeneous 5G-TSN systems requires joint scheduling between both domains. A key prerequisite for time-aware end-to-end scheduling is determining the forwarding delay for each TSN Traffic Class at every bridge, referred to as Bridge Delay (BD). Hence, to integrate 5G as a transparent TSN bridge, the 5G BD must be determined and guaranteed. Unlike wired bridges, the 5G BD relies on wireless resource management characteristics, such as the Time Division Duplex pattern and radio resource allocation procedure. In particular, traditional Uplink (UL) schedulers are optimized for throughput but often fail to meet the deadline requirements. To address this challenge, we propose a heterogeneous radio resource scheduler that integrates static and dynamic scheduling. The algorithm pre-allocates resources for time-sensitive periodic streams based on the reported BDs, ensuring alignment with the TSN mechanisms Time-Aware Shaper and Per-Stream Filtering and Policing. Meanwhile, remaining resources are dynamically allocated to non-deterministic flows using established strategies such as Proportional Fair, Max C/I, or a Quality of Service-aware priority-based scheduler. The scheduler's performance is evaluated through OMNeT++ simulations. The results demonstrate support for diverse TSN flows while ensuring deadline-aware scheduling of time-sensitive UL traffic in mobility scenarios. Periodic time-sensitive flows are end-to-end scheduled across domains, improving the resource efficiency by 28% compared to the Configured Grant baseline. While reliability is preserved, non-deterministic rate-sensitive flows benefit from the improved resource utilization, resulting in higher throughput

Problem

• Integrating 5G uplink with Time-Sensitive Networking for industrial mobility systems
• Addressing deadline failures in traditional uplink schedulers for time-sensitive traffic
• Enabling deterministic end-to-end scheduling across heterogeneous 5G-TSN networks

Innovation

• Heterogeneous radio resource scheduler combining static and dynamic scheduling
• Pre-allocates resources for time-sensitive streams using Bridge Delay
• Dynamically allocates remaining resources using established scheduling strategies