Spectrum Occupancy Prediction Exploiting Time and Frequency Correlations Through 2D-LSTM

Mehmet Ali Aygul, Mahmoud Nazzal, Ali Riza Ekti, Ali Gorcin, Daniel Benevides Da Costa, Hasan Fehmi Ates, Huseyin Arslan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

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 languageEnglish
Title of host publication2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728152073
DOIs
StatePublished - May 2020
Externally publishedYes
Event91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
Duration: May 25 2020May 28 2020

Publication series

NameIEEE Vehicular Technology Conference
Volume2020-May
ISSN (Print)1550-2252

Conference

Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020
Country/TerritoryBelgium
CityAntwerp
Period05/25/2005/28/20

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

Fingerprint

Dive into the research topics of 'Spectrum Occupancy Prediction Exploiting Time and Frequency Correlations Through 2D-LSTM'. Together they form a unique fingerprint.

Cite this