Halim Yanikomeroglu

Extreme distribution shifts in non-terrestrial networks can be mastered with few-shot adaptation by enforcing causal invariance over distributed physical fingerprints, achieving 96% AUC in previously unseen deployment environments.

A hierarchical graph attention network beats traditional machine learning models by 21% in predicting spectrum demand, offering a more reliable approach to spectrum management.

Spectrum regulators can now leverage AI to dynamically plan and allocate spectrum resources, thanks to a new data-driven approach that accurately forecasts demand with high reliability across diverse urban environments.

Accurately predicting spectrum demand across urban areas is now possible, with a model that captures 70% of the variability, paving the way for more efficient 6G network management.