Low Earth Orbit satellite Internet has recently been deployed, providing worldwide service with non-terrestrial networks. With the large-scale deployment of both non-terrestrial and terrestrial networks, limited spectrum resources will not be allocated enough. Consequently, dynamic spectrum sharing is crucial for their coexistence in the same spectrum, where accurate spectrum sensing is essential. However, spectrum sensing in space is more challenging than in terrestrial networks due to variable channel conditions, making single-satellite sensing unstable. Therefore, we first attempt to design a collaborative sensing scheme utilizing diverse data from multiple satellites. However, it is non-trivial to achieve this collaboration due to heterogeneous channel quality, considerable raw sampling data, and packet loss. To address the above challenges, we first establish connections between the satellites by modeling their sensing data as a graph and devising a graph neural network-based algorithm to achieve effective spectrum sensing. Meanwhile, we establish a joint sub-Nyquist sampling and autoencoder data compression framework to reduce the amount of transmitted sensing data. Finally, we propose a contrastive learning-based mechanism compensates for missing packets. Extensive experiments demonstrate that our proposed strategy can achieve efficient spectrum sensing performance and outperform the conventional deep learning algorithm in spectrum sensing accuracy.

翻译：近地轨道卫星互联网近期已实现部署，通过非地面网络提供全球覆盖服务。随着非地面网络与地面网络的大规模部署，有限的频谱资源将面临分配不足的挑战。因此，动态频谱共享对于二者在同频段共存至关重要，而精确的频谱感知是实现该目标的基础。然而，空间环境下的频谱感知比地面网络更具挑战性，信道条件的动态变化导致单星感知结果不稳定。为此，我们首次尝试利用多卫星的异构数据设计协同感知方案。但由于信道质量差异显著、原始采样数据量庞大及数据包丢失等问题，实现有效协同并非易事。针对上述挑战，我们首先通过将卫星感知数据建模为图结构并设计基于图神经网络的算法，建立卫星间的关联以实现高效频谱感知。同时，我们构建了联合亚奈奎斯特采样与自编码器数据压缩框架，以降低感知数据的传输量。最后，提出基于对比学习的机制以补偿丢失的数据包。大量实验表明，所提策略能够实现高效的频谱感知性能，并在感知准确率上优于传统深度学习算法。