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Spectrum Situation Awareness for Space-Air-Ground Integrated Networks Based on Tensor Computing.

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|January 23, 2024
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Summary

Spectrum situation awareness in space-air-ground integrated networks (SAGINs) is addressed using tensor computing. Tensor eigenvalues effectively characterize spectrum data, enabling novel spectrum sensing schemes for enhanced network awareness.

Keywords:
space–air–ground integrated networksspectrum situation awarenesstensor computingtensor eigenvalue

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Area of Science:

  • Network Engineering
  • Data Science
  • Signal Processing

Background:

  • Space-air-ground integrated networks (SAGINs) generate vast multidimensional heterogeneous big data.
  • Traditional methods struggle with the complexity and scale of SAGINs data.
  • Effective spectrum situation awareness is critical for SAGINs performance.

Purpose of the Study:

  • To investigate the application of tensor computing for spectrum situation awareness in SAGINs.
  • To develop a novel spectrum awareness scheme leveraging tensor data models and computations.
  • To explore the utility of tensor eigenvalues in characterizing spectrum data and enabling advanced sensing.

Main Methods:

  • A multidimensional tensor data model was designed for SAGINs.
  • Tensor decomposition and completion were employed for data dimensionality reduction and missing data imputation.
  • Tensor eigenvalues were calculated and their distributions analyzed.
  • Spectrum sensing schemes were designed based on tensor eigenvalue distributions and hypothesis testing.

Main Results:

  • Tensor computing effectively handles multidimensional heterogeneous big data from SAGINs.
  • Tensor eigenvalues provide insights into intrinsic data correlations, crucial for situation awareness.
  • A novel tensor-computing-based spectrum awareness scheme was proposed and validated.
  • Simulation results demonstrated the feasibility of spectrum awareness using tensor eigenvalues.

Conclusions:

  • Tensor computing offers a powerful paradigm for spectrum situation awareness in SAGINs.
  • Tensor eigenvalues serve as a robust statistical indicator for spectrum awareness.
  • This work opens new avenues for applying tensor theory in practical communication networks.