• English
    • français
  • Help
  •  | 
  • Contact
  •  | 
  • About
  •  | 
  • Login
  • HAL portal
  •  | 
  • Pages Pro
  • EN
  •  / 
  • FR
View Item 
  •   LillOA Home
  • Liste des unités
  • Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520
  • View Item
  •   LillOA Home
  • Liste des unités
  • Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A Novel Online Subcarrier-Wise Extreme ...
  • BibTeX
  • CSV
  • Excel
  • RIS

Document type :
Communication dans un congrès avec actes
DOI :
10.1109/WiMob52687.2021.9606391
Title :
A Novel Online Subcarrier-Wise Extreme Learning Machine Receiver for OFDM Systems
Author(s) :
Saideh, Michel [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Simon, Eric [Auteur] refId
Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN [TELICE - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Farah, Joumana [Auteur]
Conference title :
17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2021)
City :
Bologna
Country :
Italie
Start date of the conference :
2021-10-11
Publisher :
IEEE
Publication date :
2021
English keyword(s) :
Extreme Learning Machine
Interpolated Training
OFDM
Equalization
Channel Estimation
HAL domain(s) :
Sciences de l'ingénieur [physics]
English abstract : [en]
Recently, Extreme Learning Machine (ELM) started gaining interest among researchers in wireless communications as an online training solution for machine learning based receivers. ELM has proven to provide high training ...
Show more >
Recently, Extreme Learning Machine (ELM) started gaining interest among researchers in wireless communications as an online training solution for machine learning based receivers. ELM has proven to provide high training speed and global optimization capabilities. However, the number of needed training pilots is still relatively high and increases rapidly with the number of subcarriers, thus rendering its deployment impractical. In this paper, we propose subcarrier-wise ELM receivers that are robust to the increase in the number of used subcarriers; we then extend them to exploit adjacent channel knowledge, hence providing superior performance in frequency selective channels. In addition, we propose a novel training architecture based on interpolated training that saves more than 50% of the computational and spectral resources of conventional ELM receivers. We show the robustness of the proposed technique in different channel scenarios and OFDM settings by means of both practical channel measurements and numerical simulations.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520
Source :
Harvested from HAL
Université de Lille

Mentions légales
Université de Lille © 2017