Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM
View/ Open
Access
info:eu-repo/semantics/embargoedAccessDate
2020Author
Aygül, Mehmet AliNazzal, Mahmoud
Ekti, Ali Rıza
Görçin, Ali
da Costa, Daniel Benevides
Ateş, Hasan Fehmi
Arslan, Hüseyin
Metadata
Show full item recordCitation
Aygül, M. A., Nazzal, M., Ekti, A. R., Görçin, A., da Costa, D. B., Ateş, H. F. ... Arslan, H. (2020). Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM. 91st IEEE Vehicular Technology Conference, VTC Spring. Antwerp, Belgium, 25-28 May 2020. https://dx.doi.org/10.1109/VTC2020-Spring48590.2020.9129001Abstract
The identification of spectrum opportunities is a pivotal requirement for efficient spectrum utilization in cognitive radio systems. Spectrum prediction offers a convenient means for revealing such opportunities based on the previously obtained occupancies. As spectrum occupancy states are correlated over time, spectrum prediction is often cast as a predictable time-series process using classical or deep learning-based models. However, this variety of methods exploits time-domain correlation and overlooks the existing correlation over frequency. In this paper, differently from previous works, we investigate a more realistic scenario by exploiting correlation over time and frequency through a 2D-long short-term memory (LSTM) model. Extensive experimental results show a performance improvement over conventional spectrum prediction methods in terms of accuracy and computational complexity. These observations are validated over the real-world spectrum measurements, assuming a frequency range between 832-862 MHz where most of the telecom operators in Turkey have private uplink bands.
Source
91st IEEE Vehicular Technology Conference, VTC SpringVolume
2020URI
https://dx.doi.org/10.1109/VTC2020-Spring48590.2020.9129001https://hdl.handle.net/20.500.12511/6435