Context-based Latency Guarantees Considering Channel Degradation in 5G Network Slicing

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.