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

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

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

TDoA based indoor positioning over small cell 5G networks

Accurate indoor positioning is a highly requested feature in a wide range of applications, and an increasing demand is expected with the rollout of the fifth-generation (5G) cellular communication system. Although intensive research has been conducted on this topic since the 1990s, the global market still lacks a reliable, affordable, flexible, and sufficiently accurate indoor positioning system. Indoor localization over the cellular 5G network is a promising alternative for multiple reasons: no additional infrastructure costs, high availability, good control over security, and easy integration with other services due to standardization. The only questionable aspect is meeting the accuracy requirements for certain applications. However, 5G has numerous beneficial modifications and new features concerning positioning that can be exploited in the future. Our paper investigates the realization of the first TDOA-based indoor positioning system on existing 5G small cell networks, focusing primarily on the challenging effects of indoor signal propagation and possible ways to overcome them. To achieve this, we created a channel model based on real 5G measurements taken in an open-office building, and we used the obtained novel model to create a realistic simulation framework. This simulation framework was utilized to investigate the positioning performance of several algorithms. In addition to signal propagation issues, we investigate and highlight several other crucial aspects (e.g., synchronization and installation errors) that must be considered when deploying an industry-grade TDOA-based 5G positioning system

eFRADIR: An Enhanced FRAmework for DIsaster Resilience

This paper focuses on how to increase the availability of a backbone network with minimal cost. In particular, the new framework focuses on resilience against natural disasters and is an evolution of the FRADIR/FRADIR-II framework. It targets three different directions, namely: network planning, failure modeling, and survivable routing. The steady state network planning is tackled by upgrading a sub-network (a set of links termed the spine) to achieve the targeted availability threshold. A new two-stage approach is proposed: a heuristic algorithm combined with a mixed-integer linear problem to optimize the availability upgrade cost. To tackle the disaster-resilient network planning problem, a new integer linear program is presented for the optimal link intensity tolerance upgrades together with an efficient heuristic scheme to reduce the running time. Failure modeling is improved by considering more realistic disasters. In particular, we focus on earthquakes using the historical data of the epicenters and the moment magnitudes. The joint failure probabilities of the multi-link failures are estimated, and the set of shared risk link groups is defined. The survivable routing aims to improve the network's connectivity during these shared risk link group failures. Here, a generalized dedicated protection algorithm is used to protect against all the listed failures. Finally, the experimental results demonstrate the benefits of the refined eFRADIR framework in the event of disasters by guaranteeing low disconnection probabilities even during large-scale natural disasters.This article is based on work from COST Action CA15127 ("Resilient communication services protecting end-user applications from disaster-based failures" - RECODIS), supported by COST (European Cooperation in Science and Technology); http://www.cost.eu. This work was supported in part by the High Speed Networks Laboratory (HSNLab); in part by the National Research, Development, and Innovation Fund of Hungary, financed through the FK_17, KH_18, K_17, FK_20 and K_18 funding schemes, respectively, under Project 123957, Project 129589, Project 124171, Project 134604, and Project 128062; and in part by the BME through the TKP2020, Institutional Excellence Program of the National Research Development and Innovation Office in the field of Artificial Intelligence under Grant BME IE-MI-SC TKP2020. The work of Rita Girão-Silva and Teresa Gomes was supported in part by the Fundação para a Ciência e a Tecnologia (FCT), I.P. under Project UIDB/00308/2020, and in part by the ERDF Funds through the Centre's Regional Operational Program and by National Funds through FCT under Project CENTRO-01-0145-FEDER-029312

The influence of the local structure of Fe(III) on the photocatalytic activity of doped TiO(2) photocatalysts

Fe(III)-doped TiO(2) based heterogeneous photocatalysts were prepared by the sol-gel technique (S samples) or flame hydrolysis (F samples). In photocatalytic phenol decomposition, the undoped F-sample performed much better, than the undoped S one. However, for the S samples, photocatalytic activity first increased with the increasing Fe(III) concentration, and then passed through a maximum, while Fe(III)-doping in F samples significantly decreased it, even at the smallest dopant level. Since the same dopant caused opposite photocatalytic effects in the two series, their structure was systematically compared to identify the underlying chemical and/or physical reasons. The photocatalysts were first characterized by AAS, DRS, XRD and TEM methods and it has been shown that the differences in the photocatalytic activity cannot be explained by the minor variations in the bulk structural properties of TiO(2). Mossbauer and XP spectroscopic measurements performed on representative samples qualitatively proved that the local structure of Fe(III) is different in the two series. To quantify these effects, Fe-K edge X-ray absorption measurements were performed. From the pre-edge and XANES region it was learnt that Fe(III) was present in a distorted octahedral environment in both series, however, the extent of distortion is much more significant within the S than within the F one. Information obtained from the EXAFS region indicated that the structure of Fe(2)O(3) was much more ordered in the F-series then in the S one and vacancies were more abundant in the S than in the F series. Moreover, the geometry around Fe(III) systematically varied within the S-series, which could explain, why photocatalytic activity passed through a maximum with the increasing Fe(III) concentration in these samples. (C) 2011 Elsevier B.V. All rights reserved