Machine learning and optimisation in 6G networks
Project Leader: Ampalavanapillai Nirmalathas
Staff: Tansu Alpcan
Collaborators: Tansu Alpcan (Electrical and Electronic Engineering Thas Nirmalathas (Electrical and Electronic Engineering)
Primary Contact: Ampalavanapillai Nirmalathas (firstname.lastname@example.org)
Keywords: 6G x-Haul Networks; Communication networks; Internet of Things (IoT); machine learning; optimisation
Disciplines: Electrical & Electronic Engineering
The convergence of computing and networking has been accelerating with new and exciting developments in 5G/6G mobile networks with integration technology enablers such as network virtualisation and edge/fog computing and will power the development of Industry 4.0 based transformation of services across many sectors.
As new and exciting architectures emerge and legacy platforms are retired, a variety of open research problems need to be addressed such as:
- How to dynamically allocate communication and computing resources to achieve satisfatory performance?
- How to predict and react to varying communication and computing demand in modern applications?
- Which mechanisms will help managing conflicting incentives of stakeholders sharing these systems?
- How do we dimension such networks and services with competing service requirements, priorities, and deployment constraints?
This project will address these questions using know-how and methods from communication networks, optimisation, game theory, and machine learning. The resulting novel solutions will be applied to modern applications in relevant domains such as network virtualisation/slicing in wireless backhaul, 5G/6G and integration of edge/fog computing approaches, and Industry 4.0.
This project is looking for motivated students who have a strong background in communication networks, optimisation, nd machine learning with at least intermediate programming skills.