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.
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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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Abdelrahman, S.A., Khaled, O., Alaa, A., Ali, M., Mohy, I., ElDieb, A.H. (2019). Real-Time Spectrum Occupancy Prediction. In: Woungang, I., Dhurandher, S. (eds) 2nd International Conference on Wireless Intelligent and Distributed Environment for Communication. WIDECOM 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-030-11437-4_17
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DOI: https://doi.org/10.1007/978-3-030-11437-4_17
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