Mobile networks are already part of our daily lives, but what we expect from them is ever changing. Transforming today’s networks to 5G is key to keeping pace with the demands of an evolving Networked Society, where opportunities span new high-bandwidth applications, low latency powered Internet of Things (IoT) services and beyond. 5G aims to satisfy these evolving needs by providing ubiquitous connectivity for any kind of device or application that may benefit.
In order to address these challenges, the next generation wireless systems need to employ forward-looking solutions such as network function virtualization technologies, software-defined network architectures, and mobile edge computing platforms. Furthermore, in order to improve network performance and enhance user’s experience, new Machine Learning methods for big data analytics in communication networks can extract relevant information from the network data, while taking into account limited communication resources, and then leverage this knowledge for autonomic network control and management as well as service provisioning.
This PhD position aims on 1) the study of the architecture for the advanced 5G infrastructure based on the latest advances of SDN, NFV and edge computing, 2) the development of an experimentally-validated analytical framework that leverages machine learning for the design of 5G systems, and 3) the validation of the expected 5G KPIs (Key Performance Indicators) via novel services and network engineering techniques.