FEIT Research Project Database

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 (nirmalat@unimelb.edu.au)
Keywords: 6G x-Haul Networks; Communication networks; Internet of Things (IoT); machine learning; optimisation
Disciplines: Electrical & Electronic Engineering
Domains:

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:

  1. How to dynamically allocate communication and computing resources to achieve satisfatory performance?
  2. How to predict and react to varying communication and computing demand in modern applications?
  3. Which mechanisms will help managing conflicting incentives of stakeholders sharing these systems?
  4. 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.