Spectrum Occupancy

Spectrum occupancy mapping aims to accurately determine the usage of radio frequencies across space and time, crucial for efficient spectrum sharing and resource allocation in wireless communication systems. Current research focuses on improving the robustness and efficiency of occupancy detection algorithms, employing machine learning techniques like deep neural networks and federated learning, often incorporating data from multiple sensors, including unmanned aerial vehicles (UAVs). These advancements are vital for optimizing spectrum utilization in increasingly complex and crowded wireless environments, impacting areas such as 6G network planning and cognitive radio systems.