A UAV assisted lightweight spectrum cognition framework for LEO satellite internet of things links

The Low Earth Orbit Satellite Internet of Things (LEO-SIoT), with its unique advantages, provides an ideal solution for achieving true ubiquitous connectivity. However, with the surge in terminal scale, spectrum resource scarcity and interference issues have become increasingly prominent. Spectrum cognition technology is an effective means to enhance spectrum utilization and avoid interference, but traditional ground-based equipment, due to fixed positions and limited coverage, often struggles to effectively perceive spectrum usage of dispersed satellite IoT terminals with skyward-pointing beams. To address this challenge, this paper proposes Sat-SCFramework, a UAV-assisted lightweight spectrum cognition framework leveraging UAV technology to achieve efficient spectrum cognition from an aerial perspective. We establish a refined scenario model describing UAV workflow for signal acquisition, image conversion, and data backhaul. Based on this, Sat-SCFramework accomplishes three typical spectrum cognition tasks: spectrum occupancy sensing, link quality assessment, and signal feature extraction. The core is Sat-SCformer, a novel lightweight deep learning algorithm for automatic classification of subcarrier modulation types. Sat-SCformer employs a dual-branch architecture, utilizing lightweight CNN to capture local features and efficient Transformer to mine global dependencies, achieving adaptive feature fusion and precise classification through multi-scale fusion and KAN modules. Finally, we build an experimental platform based on USRP X310, validating the framework through comprehensive and ablation experiments. Results demonstrate that Sat-SCformer achieves 98.25% accuracy with only 1.01 M parameters and 0.20G FLOPs, confirming the design’s rationality and engineering value.