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Spectrum prediction and interference detection for satellite communications

Spectrum prediction and interference detection for satellite communications

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Spectrum monitoring and interference detection are crucial for the satellite service performance and the revenue of SatCom operators. Interference is one of the major causes of service degradation and deficient operational efficiency. Moreover, the satellite spectrum is becoming more crowded, as more satellites are being launched for different applications. This increases the risk of interference, which causes anomalies in the received signal, and mandates the adoption of techniques that can enable the automatic and realtime detection of such anomalies as a first step toward interference mitigation and suppression. In this chapter, we present a machine learning (ML)-based approach which is able to guarantee a real-time and automatic detection of both short-term and long-term interference in the spectrum of the received signal at the base station. The proposed approach can localize the interference both in time and in frequency and is universally applicable across a discrete set of different signal spectra. We present experimental results obtained by applying our method to real spectrum data from the Swedish Space Corporation. We also compare our ML-based approach to a model-based approach applied to the same spectrum data and used as a realistic baseline. Experimental results show that our method is a more reliable interference detector.

Chapter Contents:

  • 63.1 Introduction
  • 63.2 Proposed approach
  • 63.2.1 Notation and assumptions
  • 63.2.2 Method
  • 63.2.3 Long short-term memory
  • 63.3 Experimental results
  • 63.3.1 Dataset
  • 63.3.2 Architecture and training
  • 63.3.3 Results
  • 63.4 Comparison with a model-based approach
  • 63.4.1 Notation
  • 63.4.2 Method
  • 63.4.3 Experimental results
  • 63.4.4 Comparison
  • 63.5 Conclusion
  • Acknowledgments
  • References

Inspec keywords: telecommunication computing; learning (artificial intelligence); satellite communication; radio spectrum management; radiofrequency interference

Other keywords: machine learning-based approach; long-term interference mitigation; model-based approach; received signal spectra; base station; satellite spectrum monitoring; Swedish Space Corporation; ML-based approach; interference detector reliability; satellite communications; SatCom operators; satellite service performance

Subjects: Satellite communication systems; Communications computing; Legislation, frequency allocation and spectrum pollution; Knowledge engineering techniques; Electromagnetic compatibility and interference

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