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Real-Time Spectrum Occupancy Prediction

  • S. A. Abdelrahman
  • Omar Khaled
  • Amr Alaa
  • Mohamed Ali
  • Injy Mohy
  • Ahmed H. ElDieb
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 27)

Abstract

Spectrum shortage and scarcity have been a strong research motivation for implementing cognitive radio to utilize the electromagnetic spectrum efficiently. Several spectrum sensing techniques have been proposed to trace and detect the primary user activity. Therefore, we can fully utilize the frequency spectrum. In this chapter, we propose an artificial neural network-based energy detection method to maximize the probability of detecting primary users in varying and dynamic environmental conditions. This is achieved by deploying cognitive engines in software-defined radios outside of the traditional simulation environment to realize the reliability of detection for real-time and over-the-air transmission. Therefore, the neural network-based energy detection algorithm is usually employed for classifying whether the channel is free or occupied with a remarkable increase in the continuous sensing and prediction accuracy in real time.

Notes

Acknowledgements

We would like to thank Virginia Tech CORNET Testbed. This work would not have been possible without the open remote access to their computational resources.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • S. A. Abdelrahman
    • 1
  • Omar Khaled
    • 2
  • Amr Alaa
    • 2
  • Mohamed Ali
    • 2
  • Injy Mohy
    • 2
  • Ahmed H. ElDieb
    • 2
  1. 1.Scuola Superiore Sant’AnnaPisaItaly
  2. 2.CIC - Canadian International CollegeCairoEgypt

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