Positioning in 5G and 6G Networks—A Survey

Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios

Resilient Control Plane Design for Virtualized 6G Core Networks

With the advent of 6G and its mission-critical and tactile Internet applications running in a virtualized environment on the same physical infrastructure, even the shortest service disruptions have severe consequences for thousands of users. Therefore, the network hypervisors, which enable such virtualization, should tolerate failures or be able to adapt to sudden traffic fluctuations instantaneously, i.e., should be well-prepared for such unpredictable environmental changes. In this paper, we propose a latency-aware dual hypervisor placement and control path design method, which protects against single-link and hypervisor failures and is ready for unknown future changes. We prove that finding the minimum number of hypervisors is not only NP-hard, but also hard to approximate. We propose optimal and heuristic algorithms to solve the problem. We conduct thorough simulations to demonstrate the efficiency of our method on real- world optical topologies, and show that with an appropriately selected representative set of possible future requests, we are not only able to approach the maximum possible acceptance ratio but also able to mitigate the need of frequent hypervisor migrations for most realistic latency constraints

Disaster-Resilient Network Upgrade

The manifold impacts of the current pandemic have highlighted the importance of reliable communication networks and services. As more and more people and services rely on this critical infrastructure, single link failure resilience is not sufficient anymore; networks must be disaster resilient. In this paper, we analyze the effects of disasters from a connectivity perspective and focus on reducing the likelihood of network disconnection in the event of a disaster through targeted link upgrades. In particular, we formalize the generalized Minimum Cost Disaster Resilient Network Upgrade Problem (DNP) (based on the previously published eFRADIR framework). We prove that this problem is NP-hard and as hard to approximate as the Knapsack Problem (KP). We present several methods for solving the DNP, in particular an ILP and two heuristics. We evaluate their performance on real networks and earthquake data and show that the upgrade cost of our disconnection probability based heuristic is only 3.5% higher than the optimum, while its resource consumption is negligible compared to the ILP