Society’s reliance on telecommunication infrastructure continues to grow as both energy and telecommunications solutions share mutual dependencies. This dependency calls for increased monitoring of fibre networks and fibre optic subsea cables, which serve as the backbone of connectivity for all digital communications.

Distributed Acoustic Sensing (DAS) and State of Polari- sation (SoP) monitoring provide real-time measurements of vibrations and potential mechanical interference which could impact the cable. Implementation of these sensing technologies mitigate risk to the infrastructure, preventing equipment failure and downtime. In particular, any unexpected movements caused by subsea activities such as passing trawlers, hooking of equipment on the seabed, and any geophysical phenomena can be traced so that appropriate action could be taken.

This article will explain the capability and benefits of the monitoring system and how Tampnet utilises this system for monitoring its vast subsea and terrestrial cable systems.

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.