I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Model-Assisted In-Orbit Intelligent Computation for Satelli..:
, In:
2023 IEEE International Conference on Communications Workshops (ICC Workshops)
,
Gong, Yongkang
;
Yao, Haipeng
;
Nallanathan, Arumugam
. - p. 586-591 , 2023
Link:
https://doi.org/10.1109/ICCWorkshops57953.2023.1028359
RT T1 <cTitle>2023 IEEE International Conference on Communications Workshops (ICC Workshops)</cTitle>: T1 <Title>Model-Assisted In-Orbit Intelligent Computation for Satellite-Ground Integrated Networks</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10283599&Exemplar=1&LAN=DE A1 Gong, Yongkang A1 Yao, Haipeng A1 Nallanathan, Arumugam A1 Hossain, Ekram YR 2023 SN 2694-2941 K1 Deep learning K1 Computational modeling K1 Conferences K1 Stochastic processes K1 Reinforcement learning K1 Time-varying channels K1 Energy efficiency K1 Satellite-ground (SG) integrated networks K1 model-assisted in-orbit deep reinforcement learning (MAI-DRL) policy K1 in-orbit edge intelligence SP 586 OP 591 LK http://dx.doi.org/https://doi.org/10.1109/ICCWorkshops57953.2023.10283599 DO https://doi.org/10.1109/ICCWorkshops57953.2023.10283599 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
