Sixth-generation communication networks must be highly dependable due to the foreseen connectivity of critical infrastructures through them. Dependability is a compound metric of four well-known concepts—reliability, availability, safety, and security. Each of these concepts have unique consequences for the success of 6G technologies and applications. Using these concepts, we explore the dependability of 6G networks in this article. Due to the vital role of machine learning in 6G, the dependability of federated learning, as a distributed machine learning technique, has been studied. Since mission-critical applications (MCAs) are highly sensitive in nature, needing highly dependable connectivity, the dependability of MCAs in 6G is explored. Henceforth, this article provides important research directions to promote further research in strengthening the dependability of 6G networks.

This paper presents some key findings w.r.t. the Radio Resource Management (RRM) in wireless Backhaul (BH) of mmWave networks. First, the envisioned design of mmWave backhaul architecture is outlined highlighting the most important newly needed functional blocks and in what they differ from a non-mmWave architectures. Next, the challenges and functionality of RRM techniques are discussed, focusing on Routing and Link scheduling algorithms in such BH architecture. Furthermore different possible interactions between RRM functions are explored. Finally, preliminary analytical and experimental study on the performance of different link scheduling and routing functions for mmWave backhauling are provided, highlighting in particular the impact of traffic load and dynamic route selection on BH End to End delay.

Static Wireless Mesh Networks (WMN) based on high capacity pencil-beam millimeter wave (mmWave) links offer an attractive solution for small cell backhauling in 5G networks. However, the delay targets in 5G are such that it is quite challenging to develop adequate link scheduling and routing mechanisms. Furthermore, the susceptibility of mmWave radio links to adverse weather conditions and other obstructions in the line-of-sight line makes it necessary that routing is able to cope with frequent link degradations and failures. We propose an approach that divides the problem into two parts - finding first a suitable set of so called primary paths and then optimizing the link schedules for those paths. In this paper, we present a routing algorithm that produces the required primary paths and several backup paths for all source-destination pairs. Furthermore, we describe a hierarchical fault recovery system that includes various protection and restoration methods that are acting in different time scales to ensure fast reaction to failures and then optimal adaptation to the changed situation.

We describe a novel wireless backhaul solution, called Self-Optimizing Wireless Mesh Network (SWMN), which is targeted for interconnecting 5G small cell nodes cost-efficiently and smartly. SWMN is not only highly resilient and scalable, but it is also equipped with advanced Self-Organizing Network (SON) capabilities offering autonomous network configuration and traffic engineering features. The key aspect of SWMN is an innovative network intelligence engine coupled with beam steerable pencil beam millimeter wave (mmW) high capacity backhaul radio.