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.

利用多颗卫星的多样数据进行协作感知以及利用图神经网络进行有效的频谱感知，同时采用联合子 - Nyquist 采样和自动编码器数据压缩框架以及对丢失数据进行对比学习，以实现高效的频谱感知性能并在频谱感知准确性上超越传统深度学习算法。