Spectrum Occupancy Prediction Exploiting Time and Frequency Correlations Through 2D-LSTM - Aygul, Mehmet Ali; Nazzar, Mahmoud; Ekti, Ali Riza; Gorcin, Ali; da Costa, Daniel Bcncvidcs; Ates, Hasan Fehmi; Arslan, Huseyin (IEEE, 2020)
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.
Keywords: Deep learning; frequency correlation; real-world spectrum measurement; spectrum occupancy prediction; COGNITIVE RADIO; Automation & Control Systems; Computer Science; Telecommunications; Transportation