End-to-end delay performance analysis of the 5G-TSN network using network calculus

Phase-Aware TAS Scheduling for Minimizing TDD-Induced Worst-Case End-to-End Delay in 5G–TSN Integrated Networks

The integration of Time-Sensitive Networking (TSN) and 5G is essential for deterministic industrial communication over mixed wired–wireless networks. In 3GPP TSN–5G integration, the 5G System (5GS) is modeled as a logical TSN bridge for Time-Aware Shaper (TAS) scheduling. However, Time Division Duplex (TDD) operation in 5GS introduces the transmission waiting time, and conventional bridge-delay-based scheduling must rely on conservative worst-case assumptions.In this paper, we analyze the periodic structure of TDD-induced waiting time and show that its worst-case value is determined by the TDD pattern, TSN flow period, and their phase offset. Based on this insight, we propose a phase-aware TAS scheduling method that derives a mapping between phase offsets and worst-case waiting times and incorporates it into E2E TAS scheduling. Simulations under multiple TSN flow periods and TDD patterns show that the proposed method consistently reduces worst-case waiting time compared with the conventional approach. Multiple flow evaluations also show that multiple TSN flows can be scheduled while satisfying E2E delay constraints under the evaluated configuration. This enables more flexible deployment of time-critical industrial IoT applications, including mobile robot control, motion control, and factory automation over mixed wired–wireless networks.

End-to-End Routing for Jointly Ultra-Service and Regular-Service Flows in TSN: An Evolutionary Transformer-Based DRL Approach

The development of immersive video service and large-scale cluster computing technology further expand the potential application scope of time-sensitive networks (TSN). In the delivery network for these emerging services, Ultra-Service Flows (USFs), characterized by ultra-high bandwidth and deterministic latency, have become the most representative traffic type. Therefore, the route scheduling for hybrid deployment of Regular-Service Flows (RSFs) and USF has become an unavoidable issue within a deterministic domain. However, existing research has not thoroughly investigated routing issues for the hybrid deployment of USF and RSF since the significant differences between them. To resolve this issue, a multi-objective optimization model is designed in this paper, in which three key factors are comprehensively considered: the path blocking degree of USF, the available bandwidth rate, and the end-to-end latency of RSF. Subsequently, we propose a cooperative framework where a Transformer-DRL agent, enforced by validity-constraint masking, generates high-quality initial populations to “warm start” NSGA-II. This hybrid design replaces random initialization, effectively resolving the evolutionary “cold start” issue in large-scale topologies while ensuring routing feasibility. The simulation results demonstrate that the algorithm proposed here in significantly improves performance and generalization capabilities, improving the RSF’s overall latency, the USF’s path-blocking degree, and the available bandwidth rate by 10.526%, 14.102%, and 14.286%, respectively.

B-CUBE: a burst-aware 5G-TSN architecture and its large-scale mixed-flow scheduling

Cross-Layer Scheduling Optimization of 5G-TSN Integrated Networks Based on QoS Mapping and Joint Scheduling

Simulation results demonstrate that the proposed QoS-mapping-based no-wait latency-balanced joint scheduling (QMLB-JS) algorithm improves the end-to-end deterministic transmission capability of the integrated 5G-TSN network.

AVB-Aware Optimized Routing and Scheduling of Time-Triggered Traffic in Time-Sensitive Networks

Time-Sensitive Networking (TSN) supports mixed-criticality communication by integrating Time-Triggered (TT) and Audio Video Bridging (AVB) traffic within a unified network infrastructure. While TT flows benefit from deterministic scheduling through the Time-Aware Shaper (TAS), their presence can increase the worst-case delay (WCD) experienced by AVB traffic. However, many existing AVB-aware TT scheduling approaches incur high computational costs and lack a theoretical foundation for analyzing the impact of TT routing on AVB performance. To address these limitations, this article presents a unified routing and scheduling framework that jointly optimizes TT communication while systematically improving AVB performance. At the core of our method is a network calculus-based analysis that derives a theoretical lower bound on AVB WCD under TT interference. This bound is consistently leveraged in both the routing and scheduling stages: first, to define a flow-level metric called Impact on WCD (IoW) that guides AVB-aware routing decisions; and second, to introduce an AVB-Aware Idle Constraint that regulates TT offsets to shape residual bandwidth for AVB traffic. Extensive experiments across diverse topologies and traffic patterns demonstrate that the proposed framework significantly improves AVB schedulability and delay bounds while maintaining TT feasibility with low computational overhead. These results confirm the practicality and effectiveness of a tightly integrated approach to TSN configuration for mixed-criticality systems.

QoS-Aware End-to-End Transmission Scheduling for Space--Air--Ground Integrated Networks

Space–air–ground integrated networks (SAGINs) break through the coverage and capacity limitations of terrestrial networks, providing seamless, high-bandwidth, and highly reliable communication services in remote areas. For end-to-end transmission in the Internet of Things (IoT), the store-and-forward architecture transmits data packets in a best-effort way, resulting in unpredictable latency. The exclusive occupation of links by flows leads to a significant drop in resource utilization. To satisfy deterministic end-to-end communication, this article proposes a quality-of-service (QoS)-aware end-to-end transmission architecture for SAGINs. It comprises two core types of links: access links assisted by high-altitude platforms (HAPs) and backhaul links built around the low-Earth orbit (LEO) satellite. End-to-end flows will be allocated time slots and transmitted hop-by-hop within a single frame to meet deterministic QoS requirements. In this architecture, an optimization problem is designed to maximize the number of successfully scheduled flows with different QoS requirements. To solve the non-deterministic polynomial hard (NP-hard) mixed-integer nonlinear program in dynamic scheduling scenarios, a joint access and transmission heuristic algorithm is proposed. Specifically, to ensure efficient transmission and improve resource utilization, concurrent end-to-end flows are first processed with conflict filtering, followed by hop-by-hop scheduling with the priority based on the least number of required time slots. The simulation results show that, compared to other baseline schemes, the proposed scheme achieves a significant improvement in scheduling performance.