Deep Learning Meets Cognitive Radio: Predicting Future Steps

SHENFIELD, Alex, KHAN, Zaheer and AHMADI, Hamed (2020). Deep Learning Meets Cognitive Radio: Predicting Future Steps. In: 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). IEEE.

2020_shenfield_et_al_vtc.pdf - Accepted Version
All rights reserved.

Download (244kB) | Preview
Official URL:
Link to published version::


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.

Item Type: Book Section
Additional 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 ISSN: 2577-2465 25-28 May 2020 Antwerp, Belgium
Identification Number:
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 11 Mar 2020 10:21
Last Modified: 18 Mar 2021 00:04

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics