This white paper on AI and ML as enablers of beyond 5G (B5G) networks is based on contributions from almost 20 5G PPP projects, coordinated through the 5G PPP Technology Board, that research, implement and validate 5G and B5G network systems. The paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for enhancing 5G and B5G networks.

Being able to automatically link social media and satellite imagery holds large opportunities for research, with a potentially considerable impact on society. The possibility of integrating different information sources opens in fact to new scenarios where the wide coverage of satellite imaging can be used as a collector of the fine-grained details provided by the social media. Remote-sensed data and social media data can well complement each other, integrating the wide perspective provided by the satellite view with the information collected locally, being it textual, audio, or visual. Among the possible applications, natural disasters are certainly one of the most interesting scenarios, where global and local perspectives are needed at the same time.
In this paper, we present a system called JORD that is able to autonomously collect social media data (including the text analysis in local languages) about technological and environmental disasters, and link it automatically to remote-sensed data. Moreover, in order to ensure the quality of retrieved information, JORD is equipped with a hierarchical filtering mechanism relying on the temporal information and the content analysis of retrieved multimedia data.
To show the capabilities of the system, we present a large number of disaster events detected by the system, and we evaluate both the quality of the provided information about the events and the usefulness of JORD from potential users viewpoint, using crowdsourcing.

This paper addresses the problem of floods classification and floods aftermath detection based onboth social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods is carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches.

The growing complexity of communication networks and the explosion of network traffic have made the task of managing these networks exceedingly hard. A potential approach for striking this increasing complexity is to build an autonomous self-driving network that can measure, analyze and control itself in real time and in an automated fashion with- out direct human intervention. In this thesis, we focus on realizing such an autonomous network leveraging state-of-the-art networking technologies along with artificial intelli- gence and machine learning techniques. Toward this goal, we exploit different learning paradigms to automate network management. First, we propose supervised machine learning methods to detect increases in delays in mobile broadband networks. Further, considering the challenges of supervised learning in networking applications, we present a novel real-time distributed architecture for detecting anomalies in mobile network data in an unsupervised fashion. It also involves a collaborative framework for knowledge sharing between the distributed probes in the network to improve the overall system accuracy. Second, we propose a novel deep reinforcement learning based control framework for op- timizing resources utilization while minimizing performance degradation in multi-slice Radio Access Network (RAN) through a set of diverse control actions. We explore both centralized and distributed control architectures. Last, we design a framework for timely collecting telemetry, detecting and attributing outages in mobile networks. We evaluate our framework on a software defined virtualised testbed that resembles a cloudified mobile network.