Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations. In this work we proposed a Deep Learning (DL) approach to learn the channel occupancy model and predict its availability in the next time slots. Our results show that the proposed DL approach outperforms existing works by 5%. We also show that our proposed DL approach predicts the availability of channels accurately for more than one time slot.
Shenfield Alex, Khan Zaheer, Ahmadi Hamed
A4 Article in conference proceedings
Place of publication:
Proceedings of the 91st IEEE Vehicular Technology Conference, VTC Spring 2020. Antwerp, Belgium 25-28 May 2020
A. Shenfield, Z. Khan and H. Ahmadi, “Deep Learning Meets Cognitive Radio: Predicting Future Steps,” 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020, pp. 1-5, doi: 10.1109/VTC2020-Spring48590.2020.9129042
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