Deep Learning Meets Cognitive Radio

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

Publication type:
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

channel occupancy models, cognitive radio, Deep learning


Full citation:
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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