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- Title
CSL-SFNet for Cooperative Spectrum Sensing in Cognitive Satellite Network with GEO and LEO Satellites.
- Authors
Kai Yang; Shengbo Hu; Xin Zhang; Tingting Yan; Manqin Zhu
- Abstract
In a cognitive satellite network (CSN) with GEO and LEO satellites, there is a large propagation losses between the sensing satellite and the ground station. The results of spectrum sensing from a single satellite may be inaccurate, which will create serious interference in the primary satellite system. Cooperative spectrum sensing (CSS) has become the key technology for solving the above problems in recent years. However, most of the current CSS techniques are model-driven. They are difficult to model and implement in CSNs since their detection performance is strongly dependent on an assumed statistical model. Thus, we propose a novel CSS scheme, which uses convolutional neural networks (CNNs), self-attention (SA) modules, long short-term memory networks (LSTMs), and soft fusion networks, called CSL-SFNet. This scheme combines the advantages of CNNs, SA modules, and LSTMs to extract the features of the input signals from the spatial and temporal domains. Additionally, the CSL-SFNet makes use of a novel soft fusion technique that improves detection performance while also considerably reducing communication overhead. The simulation results demonstrate that the proposed algorithm can achieve a detection probability of 90% when the signal-tonoise ratio is −20 dB; it has a shorter running time and always outperforms the other CSS algorithms.
- Subjects
ORBIT determination; CONVOLUTIONAL neural networks; EARTH stations
- Publication
IET Signal Processing (Wiley-Blackwell), 2024, Vol 2024, p1
- ISSN
1751-9675
- Publication type
Article
- DOI
10.1049/2024/5897908