Research
Learning-driven wireless systems
Research is carried out at the ICBNET and MFOL laboratories of the School of ECE, NTUA, combining system-level simulation in MATLAB with data-driven model development in Python.
Adapting to the channel
The receiver sees noisy symbols around the ideal constellation points. As the channel improves, a denser modulation is selected: currently 16-QAM. Drag the slider to change the signal-to-noise ratio. Deciding such parameters under uncertainty, at scale and in real time, is where learning-based radio resource management comes in.