Deep Learning for Satellites Based Spectrum Sensing Systems: A Low Computational Complexity Perspective
Xiaojin Ding, Tao Ni, Yulong Zou, Gengxin Zhang
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> .