5 papers
Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks
Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu
The growing demand for wireless connectivity, combined with limited spectrum resources, calls for more efficient spectrum management. Spectrum sharing is a promising approach; howe…
AI-Enhanced Spatial Cellular Traffic Demand Prediction with Contextual Clustering and Error Correction for 5G/6G Planning
Mohamad Alkadamani, Colin Brown, Halim Yanikomeroglu
Accurate spatial prediction of cellular traffic demand is essential for 5G NR capacity planning, network densification, and data-driven 6G planning. Although machine learning can f…
Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand
Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu
In the diverse landscape of 6G networks, where wireless connectivity demands surge and spectrum resources remain limited, flexible spectrum access becomes paramount. The success of…
AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation
Colin Brown, Mohamad Alkadamani, Halim Yanikomeroglu
Accurately forecasting spectrum demand is a key component for efficient spectrum resource allocation and management. With the rapid growth in demand for wireless services, mobile n…
A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks
Mohamad Alkadamani, Halim Yanikomeroglu, Amir Ghasemi
The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum shari…