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Authors:
Zhu, Yazhou; Hofmann, Christian A.; Knopp, Andreas 
Document type:
Konferenzbeitrag / Conference Paper 
Title:
Machine Learning-based Flexible Payload Power Resource Allocation for Non-orthogonal SATCOM 
Title of conference publication:
GLOBECOM 2022 - 2022 IEEE Global Communications Conference 
Conference title:
IEEE Global Communications Conference (2022, Rio de Janeiro) 
Venue:
Rio de Janeiro 
Year of conference:
2022 
Date of conference beginning:
04.12.2022 
Date of conference ending:
08.12.2022 
Place of publication:
Piscataway, NJ 
Publisher:
IEEE 
Year:
2023 
Pages from - to:
2369-2375 
Language:
Englisch 
Subject:
Satellite Communications 
Abstract:
To meet the actual traffic demand, this work applies machine learning-based flexible payload power resource-allocation for non-orthogonal SATCOM. Specifically, a tailored deep neural network (DNN) architecture with a customized loss function is trained to intelligently allocate payload power resources among both the beams and users, by learning the undercover structure of its input (i.e., unsupervised learning). Since the DNN-based scheme doesn't need signaling and real-time information exchange...    »
 
ISBN:
978-1-6654-3540-6 ; 978-1-6654-3541-3 
Department:
Fakultät für Elektrotechnik und Informationstechnik 
Institute:
EIT 3 - Institut für Informationstechnik 
Chair:
Knopp, Andreas 
Research Hub UniBw M:
SPACE 
Open Access yes or no?:
Nein / No