Research Article
Spectrum Sensing and Prediction for 5G Radio
@INPROCEEDINGS{10.1007/978-3-030-72802-1_13, author={Małgorzata Wasilewska and Hanna Bogucka and Adrian Kliks}, title={Spectrum Sensing and Prediction for 5G Radio}, proceedings={Big Data Technologies and Applications. 10th EAI International Conference, BDTA 2020, and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, December 11, 2020, Proceedings}, proceedings_a={BDTA \& WICON}, year={2021}, month={7}, keywords={Spectrum sensing Spectrum prediction Machine learning 5G LTE Convolutional neural network Recurrent neural network Neural network Deep learning}, doi={10.1007/978-3-030-72802-1_13} }- Małgorzata Wasilewska
Hanna Bogucka
Adrian Kliks
Year: 2021
Spectrum Sensing and Prediction for 5G Radio
BDTA & WICON
Springer
DOI: 10.1007/978-3-030-72802-1_13
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.