Volume 43 Issue 3
Jul.  2023
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Article Navigation >  Chinese Journal of Space Science  > 2023  >  43(3): 567-575 Open Access
YANG Kai, HU Shengbo, ZHANG Xin. Spectrum Sensing for Combined GEO and LEO Satellites Based on Bi-LSTM and Bayesian Likelihood Ratio Test (in Chinese). Chinese Journal of Space Science, 2023, 43(3): 567-575 doi: 10.11728/cjss2023.03.2022-0017
Citation: YANG Kai, HU Shengbo, ZHANG Xin. Spectrum Sensing for Combined GEO and LEO Satellites Based on Bi-LSTM and Bayesian Likelihood Ratio Test (in Chinese). Chinese Journal of Space Science, 2023, 43(3): 567-575 doi: 10.11728/cjss2023.03.2022-0017
shu

Spectrum Sensing for Combined GEO and LEO Satellites Based on Bi-LSTM and Bayesian Likelihood Ratio Test

doi: 10.11728/cjss2023.03.2022-0017 cstr: 32142.14.cjss2023.03.2022-0017
  • 1.

    School of Big Data and Information Engineering, Guizhou University, Guiyang 550025

  • 2.

    Intelligent Information Processing Research Institute, Guizhou Normal University, Guiyang 550001

  • Received Date: 2022-05-05
  • Rev Recd Date: 2022-11-29
    • Available Online: 2022-12-10
    Abstract
  • With LEO mega satellite constellations coming into operation, the available spectrum resources are more crowded. To improve spectrum utilization, the cognitive satellite communication network composed of GEO relay satellites and LEO satellites has become one of the important candidate technologies to solve the above problem. In this scenario, LEO satellites are permitted to access the authorized spectrum of the GEO satellites through spectrum sensing technology. To avoid interferences from secondary users, spectrum sensing, which is used to quickly determine the presence or absence of primary users, is the most critical step in the scenario of cognitive satellite communication. Since most current spectrum sensing algorithms are model-driven, they rely heavily on the predetermined statistical model for their detection performance, which makes it more difficult to be modeled and deployed in satellite communication scenarios with complex channel environments. In this paper, we first analyze the fluctuation of the Signal-to-Noise Ratio (SNR) at the LEO satellite’s receiving end with the satellite-to-ground link loss model. The results show that the SNR’s fluctuation reaches 14 dB during satellite transit. Secondly, in this complex channel environment, a spectrum sensing algorithm combining a Bidirectional Long Short-Term Memory (Bi-LSTM) network and a Bayesian likelihood ratio test is proposed. The algorithm can automatically learn hidden features from the primary user signals and make final decisions without requiring any prior knowledge of the primary user signals. Additionally, according to the Neyman-Pearson criterion, we design a threshold-based detection mechanism at the output of the Bi-LSTM network, which can conveniently control the false alarm probability. Finally, the simulation results show that even with an SNR of –14 dB, the proposed algorithm achieves an excellent detection performance of 83% and always outperforms convolutional neural networks, multilayer perceptrons, and model-driven energy detection algorithms.

     

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