In the foreseeable future, highly automated mobile systems, such as vehicles, robots, UAVs, or trains, will be confronted with difficult situations that require external support. The availability of such external support corresponds to level 4 driving automation and is an essential feature in current robotaxis and automated public transportation. While the first generation of level 4 prototypes relied on safety driver support, commercial systems are gradually moving towards support by teleoperation. Designing teleoperation support for level 4 systems is an end-to-end problem involving two main research and practical challenges, the teleoperation function defining the remote human interface with its scene representation and available control functions, and the real-time communication channel involving wired and wireless segments, which must provide reliable end-to-end data transport. Both challenges are tightly linked, and combined solutions are needed to reach the required safe teleoperation. Solutions can make use of the rich sensing and control system of a level 4 vehicle, which however can only be exploited if the communication channel provides adequate real-time access.

With the introduction of the 5th generation of mobile communication networks (5G) and the on-going research for the 6th generation (6G), the adoption of mission critical applications is increasing, where flexible and highly reliable communication systems are needed. 5G has offered new and advanced features with a prominent example being the Ultra Reliable and Low Latency Communications (URLLC) features that can provide high reliability targets for critical applications. With this work we provide potential enhancements towards the 6G, that can provide reliability features for Enhanced Mobile Broadband (eMBB) traffic. Our proposal does not rely on any modification of the Radio Access Network (RAN) protocols or features but it provides an easy to implement solution to operators, reducing also the standardization overhead. More specifically, our proposed method can help to improve reliability of services by bounding the reported achievable throughput enabling the application to improve the overall data transmission. Specifically of importance is that our method does not require any Machine Learning (ML) technique alleviating the need for lengthy data collection processes, nor any other life-cycle management aspects. Our results are based on a realistic measurement campaign coming from a deployed network in an industrial environment. To demonstrate the effectiveness of our procedure, we compare it with a deep learning-based method for throughput prediction.

Mission critical applications in domains such as Industry 4.0, autonomous vehicles or Smart Grids are increasingly dependent on flexible, yet highly reliable communication systems. In this context, Fifth Generation of mobile Communication Networks (5G) promises to support mixed-criticality applications on a single unified physical communication network. This is achieved by a novel approach known as network slicing, that promises to fulfil diverging requirements while providing strict separation between network tenants. We focus in this work on hard performance guarantees by formalizing an analytical method for bounding response times in mixed-criticality 5G network slicing. We reduce pessimism considering models on workload variations.

Mission critical applications in domains such as Industry 4.0, autonomous vehicles or smart grids are increasingly dependent on flexible, yet highly reliable communication systems. The Fifth Generation of mobile Communication Networks (5G) promises to support critical communications on a single unified physical communication network through a novel approach known as network slicing. We focus in this work on context-based hard performance guarantees by formalizing an analytical method for bounding response times in critical systems. This approach allows to consider different contexts based on models of degradation of channel quality, and avoids a global highly pessimistic worst-case bound computed for worst possible channel conditions. We demonstrate that the proposed method for computing context-based response times guarantees successfully bounds results obtained in realistic mobility scenarios using a machine-learning based 5G simulation framework.