|
|
Deep learning meets cognitive radio : predicting future steps |
|---|---|
| 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: | http://urn.fi/urn:nbn:fi-fe2020102687724 |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers,
2020
|
| Publish Date: | 2020-10-26 |
| Description: |
AbstractLearning 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 |
| OADOI: | https://oadoi.org/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 |
| Subjects: | |
| 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. |