University of Oulu

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

Deep learning meets cognitive radio : predicting future steps

Saved in:
Author: Shenfield, Alex1; Khan, Zaheer2; Ahmadi, Hamed3
Organizations: 1Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom
2Centre for Wireless Communications, University of Oulu, Finland
3Department of Electronic Engineering, University of York, United Kingdom
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-10-26


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.

see all

Series: IEEE Vehicular Technology Conference
ISSN: 1090-3038
ISSN-L: 1090-3038
ISBN: 978-1-7281-5207-3
ISBN Print: 978-1-7281-4053-7
Pages: 1 - 5
Article number: 9129042
DOI: 10.1109/VTC2020-Spring48590.2020.9129042
Host publication: Proceedings of the 91st IEEE Vehicular Technology Conference, VTC Spring 2020. Antwerp, Belgium 25-28 May 2020
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Copyright information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.