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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.
