Abstract
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.
Original language | English |
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Title of host publication | 2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728152073 |
DOIs | |
State | Published - May 2020 |
Externally published | Yes |
Event | 91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium Duration: May 25 2020 → May 28 2020 |
Publication series
Name | IEEE Vehicular Technology Conference |
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Volume | 2020-May |
ISSN (Print) | 1550-2252 |
Conference
Conference | 91st IEEE Vehicular Technology Conference, VTC Spring 2020 |
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Country/Territory | Belgium |
City | Antwerp |
Period | 05/25/20 → 05/28/20 |
Funding
ACKNOWLEDGEMENT This publication was made possible by NPRP12S-0225-190152 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the author[s]. The work of D. B. da Costa was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) 2221 Programme.
Keywords
- Deep learning
- frequency correlation
- real-world spectrum measurement
- spectrum occupancy prediction