Reference : Unsupervised Learning for User Scheduling in Multibeam Precoded GEO Satellite Systems
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Electrical & electronics engineering
Computational Sciences
http://hdl.handle.net/10993/51241
Unsupervised Learning for User Scheduling in Multibeam Precoded GEO Satellite Systems
English
Ortiz Gomez, Flor de Guadalupe mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Lagunas, Eva mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
9-Jun-2022
Yes
International
European Conference on Networks and Communications (EuCNC) and the 6G Summit
from 7-06-2022 to 10-06-2022
[en] satellite communications ; multicast precoding ; unsupervised learning
[en] Future generation SatCom multibeam architectures will extensively exploit full-frequency reuse schemes together with interference management techniques, such as precoding, to dramatically increase spectral efficiency performance. Precoding is very sensitive to user scheduling, suggesting a joint precoding and user scheduling design to achieve optimal performance. However, the joint design requires solving a highly complex optimization problem which is unreasonable for practical systems. Even for suboptimal disjoint scheduling designs, the complexity is still significant. To achieve a good compromise between performance and complexity, we investigate the applicability of Machine Learning (ML) for the aforementioned problem. We propose three clustering algorithms based on Unsupervised Learning (UL) that facilitate the user scheduling decisions while maximizing the system performance in terms of throughput. Numerical simulations compare the three proposed algorithms (K-means, Hierarchical clustering, and Self-Organization) with the conventional geographic scheduling and identify the main trade-offs.
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/51241
FnR ; FNR16193290 > Eva Lagunas > SmartSpace > Leveraging Artificial Intelligence To Empower The Next Generation Of Satellite Communications > 01/04/2022 > 31/03/2025 > 2021

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Limited access
User_scheduling_based_on_Clustering_Techniques (43).pdfAuthor preprint2 MBRequest a copy

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.