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Spectrum Sensing and Prediction for 5G Radio

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Big Data Technologies and Applications (BDTA 2020, WiCON 2020)

Abstract

In future wireless networks, it is crucial to find a way to precisely evaluate the degree of spectrum occupation and the exact parameters of free spectrum band at a given moment. This approach enables a secondary user (SU) to dynamically access the spectrum without interfering primary user’s (PU) transmission. The known methods of signal detection or spectrum sensing (SS) enable making decision on spectrum occupancy by SU. The machine learning (ML), especially deep learning (DL) algorithms have already proved their ability to improve classic SS methods. However, SS can be insufficient to use the free spectrum efficiently. As an answer to this issue, the prediction of future spectrum state has been introduced. In this paper, three DL algorithms, namely NN, RNN and CNN have been proposed to accurately predict the 5G spectrum occupation in the time and frequency domain with the accuracy of a single resource block (RB). The results have been obtained for two different datasets: the 5G downlink signal with representation of daily traffic fluctuations and the sensor-network uplink signal characteristic for IoT. The obtained results prove DL algorithms usefulness for spectrum occupancy prediction and show significant improvement in detection and prediction for both low signal-to-noise ratio (SNR) and for high SNR compared with reference detection/prediction method discussed in the paper.

This work was supported by the DAINA project no. 2017/27/L/ST7/03166 “Cognitive Engine for Radio environmenT Awareness In Networks of the future” (CERTAIN) funded by the National Science Centre, Poland.

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Correspondence to Małgorzata Wasilewska .

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Wasilewska, M., Bogucka, H., Kliks, A. (2021). Spectrum Sensing and Prediction for 5G Radio. In: Deze, Z., Huang, H., Hou, R., Rho, S., Chilamkurti, N. (eds) Big Data Technologies and Applications. BDTA WiCON 2020 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-030-72802-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-72802-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72801-4

  • Online ISBN: 978-3-030-72802-1

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