SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing

Low Earth Orbit (LEO) satellite internet, providing global connectivity through non-terrestrial networks, has intensified pressure on limited spectrum resources due to the large-scale deployment of both non-terrestrial and terrestrial networks. Consequently, dynamic spectrum sharing is essential for coexistence, with accurate spectrum sensing being critical. However, single-satellite spectrum sensing is prone to instability from fluctuating channel conditions, which can severely degrade accuracy. To address this, we propose a multi-satellite collaborative sensing approach. Collaborative spectrum sensing faces challenges such as channel heterogeneity among satellites, limited data transmission capacity between satellites and ground stations, and the impact of packet loss on transmitted data. To overcome these issues, we model the sensing data as a graph and leverage a graph learning-based algorithm to enhance sensing accuracy. Additionally, we adopt a hybrid data compression strategy that combines sub-Nyquist sampling with an autoencoder to significantly reduce the volume of transmitted data. A contrastive learning-based mechanism is also integrated to mitigate the effects of packet loss. Extensive experiments demonstrate that our multi-satellite approach improves spectrum sensing performance under unstable channel conditions. Even in low signal-to-noise ratio conditions, the method remains robust. Furthermore, our graph learning technique outperforms traditional deep learning methods in terms of accuracy.