Paper published in a book (Scientific congresses, symposiums and conference proceedings)
NGSO-To-GSO Satellite Interference Detection Based on Autoencoder
SAIFALDAWLA, Almoatssimbillah; ORTIZ GOMEZ, Flor de Guadalupe; LAGUNAS, Eva et al.
2023In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Toronto, Canada, Sept. 2023
Peer reviewed Dataset
 

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Disciplines :
Computer science
Author, co-author :
SAIFALDAWLA, Almoatssimbillah  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
ORTIZ GOMEZ, Flor de Guadalupe  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
LAGUNAS, Eva  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
DAOUD, Saed Shaheer Awad  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
CHATZINOTAS, Symeon  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
External co-authors :
no
Language :
English
Title :
NGSO-To-GSO Satellite Interference Detection Based on Autoencoder
Publication date :
2023
Event name :
IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
Event organizer :
IEEE
Event place :
Toronto, Canada
Event date :
from 05-09-2023 to 08-09-2023
Audience :
International
Main work title :
IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Toronto, Canada, Sept. 2023
Publisher :
IEEE
Pages :
7
Peer reviewed :
Peer reviewed
FnR Project :
FNR16193290 - Leveraging Artificial Intelligence To Empower The Next Generation Of Satellite Communications, 2021 (01/09/2022-31/08/2025) - Eva Lagunas
Name of the research project :
U-AGR-7111 - C21/IS/16193290/SmartSpace - LAGUNAS Eva
Funders :
FNR - Luxembourg National Research Fund
Funding number :
C21/IS/16193290
Funding text :
This work is financially supported by the Luxembourg National Research Fund (FNR) under the project SmartSpace (C21/IS/16193290).
Data Set :
NGSO to GSO’s Users Interference

Dataset Description: Time series of received signal (time and frequency domain)

Commentary :
This dataset has been used in this work (please cite this reference in your work if you make use of this dataset): A. Saifaldawla, F. Ortiz, E. Lagunas, A. B. M. Adam and S. Chatzinotas, “GenAI-Based Models for NGSO Satellites Interference Detection,” in IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 904-924, 2024, doi: 10.1109/TMLCN.2024.3418933.
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since 18 July 2023

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