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
AirGNNs: Graph Neural Networks over the Air:
, In:
2023 31st European Signal Processing Conference (EUSIPCO)
,
Gao, Zhan
;
Gunduz, Deniz
- p. 963-967 , 2023
Link:
https://doi.org/10.23919/EUSIPCO58844.2023.10289977
RT T1 <cTitle>2023 31st European Signal Processing Conference (EUSIPCO)</cTitle>: T1 <Title>AirGNNs: Graph Neural Networks over the Air</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10289977&Exemplar=1&LAN=DE A1 Gao, Zhan A1 Gunduz, Deniz YR 2023 SN 2076-1465 K1 Training K1 Fading channels K1 Wireless communication K1 Location awareness K1 Degradation K1 Stochastic processes K1 Communication channels K1 Graph neural networks K1 decentralized implementation K1 wireless channel fading K1 Gaussian noise SP 963 OP 967 LK http://dx.doi.org/https://doi.org/10.23919/EUSIPCO58844.2023.10289977 DO https://doi.org/10.23919/EUSIPCO58844.2023.10289977 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
