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• W4312845020 abstract "We investigate a satellites-based spectrum sensing system in the presence of low signal-to-noise ratio (SNR) conditions. Such a low SNR is also called SNR wall in the energy detection (ED) method. To eliminate the SNR-wall effect, we propose a combined convolutional neural network and long short-term memory (C-CNN-LSTM) aided spectrum-sensing scheme. Specifically, the CNN and the LSTM are concurrently utilized, where the CNN extracts relationships among spectrum-sensing data (SSD) received at different satellites, and the LSTM excavates time-domain relationships among SSD from one satellite. Then, the outputs of the CNN and the LSTM will be combined. Performance evaluations indicate that the C-CNN-LSTM outperforms the CNN and the ED methods in terms of a higher probability of correct detection ( <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$P_{d}$</tex-math></inline-formula> ) and a lower probability of false alarm ( <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$P_{f}$</tex-math></inline-formula> ). Moreover, the C-CNN-LSTM can achieve a bit better <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$P_{d}$</tex-math></inline-formula> versus <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$P_{f}$</tex-math></inline-formula> than that of the cooperative detection DetectNets, which requires multiple DetectNets deployed on multiple sensing nodes, and is used for comparison purposes. These beneficial results demonstrate the superiority of the C-CNN-LSTM in terms of a lower implementation and computational complexity having a high <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$P_{d}$</tex-math></inline-formula> and a low <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$P_{f}$</tex-math></inline-formula> ." @default.
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• W4312845020 date "2023-01-01" @default.
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• W4312845020 title "Deep Learning for Satellites Based Spectrum Sensing Systems: A Low Computational Complexity Perspective" @default.
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• W4312845020 doi "https://doi.org/10.1109/tvt.2022.3211845" @default.
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