This project focuses on constructing radio maps for 5G communications. Radio maps are spatial databases that characterize signal power, channel gain, and interference levels across a geographic area. The key research is reconstructing accurate radio maps from sparse and irregular measurements. This research will explore data driven approaches, including machine learning techniques, to learn spatial correlations and reconstruct radio maps from limited samples. The expected contributions are novel algorithms for radio map construction validated using 5G measurements, providing practical frameworks for enhancing network intelligence and performance.
School
Electrical Engineering and Telecommunications
Research Area
Wireless communications
Suitable for recognition of Work Integrated Learning (industrial training)?
No
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
This research will leverage computational resources with machine learning frameworks, alongside access to some 5G measurement datasets and collaboration with Postdoc for writing research papers.
Expected outcomes include:
- New machine learning based methods for radio map construction
- Experimental validation
- Contributions to more efficient network planning and interference management
The supervisory team comprises academics and postdoc specialising in 5G/6G networks, ensuring comprehensive support across algorithm design, simulation, and real world data testing.
