The digitization of industries enables a rapid transformation from mass production to individualized manufacturing. Communication plays an essential role in this digital transformation; in particular, wireless communication enables a high degree of flexibility, dynamic interactions, and mobility support in production systems. This paper presents an implementation of a 5G system with Time-Sensitive Networking (TSN) and analyzes a typical industrial use case involving cloud-controlled mobile robots. A prototype setup integrating 5G in a TSN network has been completed to evaluate the 5G-TSN performance for industrial applications. The integrated 5G and TSN prototype has been evaluated with over the air tests in an industrial shopfloor using TSN features of traffic shaping and scheduling.

The stringent requirements of industrial communication, especially high reliability and real-time response, are regarded as the main bottlenecks for the widespread adoption of wireless technologies in industrial applications. The integration of 5G and Time-Sensitive Networking (TSN) protocols offers convergence of both wireless and various wired communication technologies for industrial applications. In this article, we describe our 5G and TSN integrated prototype, which achieves high reliability based on the IEEE 802.1CB Frame Replication and Elimination for Reliability (FRER) scheme. Different 5G systems have been used in various combinations to empirically study the benefits of FRER for 5G communication in real industrial environments. We evaluate the performance of our prototype and validate it for an industrial use case on Smart Sensors for Milling Processes, requiring a latency below 10 ms for 99.99% of the packets sent, which has been achieved in the measurements using FRER. This use case and the high requirements towards latency and reliability demonstrate the benefits of 5G integration with FRER for industrial production.

This paper addresses the industrial adoption gap of 5G/6G by presenting a verti-cal-agnostic Methodological Assessment Framework (MAF) that bridges network KPI with user-centric User-KPI and User-KVI to quantify techno-economic and societal value propositions from an end-user perspective. First, a detailed description of the MAF and its underlying principles is given, explaining how a use case’s value proposi-tion can be captured. Second, the MAF is applied to three different use cases from the verticals manufacturing, construction, and automotive utilizing the individual User-KPI and User-KVI for the quantification of the individual value propositions. The results show that the use of 5G can lead to enhanced process capability and reproducibility as well as increased insights into different processes. In addition, it is shown that the MAF objectively quantifies user value across diverse verticals, strengthens inter-disciplinary alignment, and can be employed to support investment decisions, based on the calcu-lated value propositions.

This paper addresses the industrial adoption gap of 5G/6G by presenting a novel, vertical-agnostic Methodological Assessment Framework (MAF). The MAF bridges the Network Key Performance Indicators (KPI) of 5G networks with user-centric User-KPIs and User-KVIs (Key Value Indicators) to quantify the techno-economic and societal value propositions of industrial 5G use cases from an end-user perspective. First, a detailed description of the MAF and its underlying principles is given, explaining how a use case’s value proposition can be captured. Second, the MAF is applied to three different industrial use cases from the verticals manufacturing, construction, and automotive utilizing the individual User-KPI and User-KVI for the quantification of the individual value propositions. The results show that the use of 5G can lead to enhanced process capability and reproducibility as well as increased insights into different processes. In addition, it is shown that the MAF objectively quantifies user value across diverse verticals and is able to strengthen interdisciplinary alignment over different verticals.

Advances in industrial 5G communication technologies and robotics create new possibilities while also increasing the complexity and variability of networked control systems. The additional throughput and lower latency provided by 5G networks enable applications such as teleoperation of machinery, flexible reconfigurable robotic manufacturing cells, or automated guided vehicles. These use cases are set up in dynamic network environments where communication latency and jitter become critical factors that must be managed. Despite the advancements in 5G technologies, such as ultra-reliable low-latency communication (URLLC), adaptive control strategies such as reinforcement learning (RL) remain critical to handle unpredictable network conditions and ensure optimal system performance in real-world industrial applications. In this paper, we investigate the potential of RL in scenarios with communication latency similar to a public 5G deployment. Our study includes an incremental improvement by utilizing long short-term memory-based neural networks in combination with proximal policy optimization in this scenario. Our findings indicate that incorporating latency into the training environment enhances the robustness and efficiency of RL controllers, especially in scenarios characterized by variable network delays. This exploration provides insights into the feasibility of using RL for networked control systems and underscores the importance of incorporating realistic network conditions into the training phase.