5G is posing high requirements on data rate and one alternative to achieve this is to increase the number of antennas on both transmitter and receiver, which is known as MIMO system.
The objective of the thesis is to study radio resource management problems in 5G networks with tools of optimization and machine learning. More precisely, multiuser joint scheduling and rank adaptation problem is formulated as an optimization problem. The student is expected to to tackle the problem under the optimization framework (e.g., linear interger programming, quadratic programing). Furthermore, the machine learning can be applied to simplify the solutions. The solutions will be implemented and evaluated (by Matlab or C). The work involves mathematical formulation, code implementations and results analysis.
- Master student in Electrical Engineering or equivalent.
- Experience in modeling and simulation.
- Knowledge of LTE/5G principles, e.g. MIMO, radio channel.
- Knowledge of machine learning.
- A solid theoretical background in areas such as signal processing or linear algebra.
Start: December 2021 (or later)
Number of positions: 1-2