Paper published in a book (Scientific congresses, symposiums and conference proceedings)
Learning-Based Resource Allocation: Efficient Content Delivery Enabled by Convolutional Neural Network
LEI, Lei; YAXIONG, Yuan; VU, Thang Xuan et al.
2019In IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
Peer reviewed
 

Files


Full Text
Learning-Based Resource Allocation Efficient Content Delivery Enabled by Convolutional Neural Network.pdf
Author postprint (239.21 kB)
Download

All documents in ORBilu are protected by a user license.

Send to



Details



Disciplines :
Electrical & electronics engineering
Author, co-author :
LEI, Lei ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
YAXIONG, Yuan ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
VU, Thang Xuan  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
CHATZINOTAS, Symeon  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
OTTERSTEN, Björn  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
no
Language :
English
Title :
Learning-Based Resource Allocation: Efficient Content Delivery Enabled by Convolutional Neural Network
Publication date :
July 2019
Event name :
IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
Event date :
07-2019
Audience :
International
Main work title :
IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019
Peer reviewed :
Peer reviewed
European Projects :
H2020 - 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems
FnR Project :
FNR11632107 - Resource Optimization For Integrated Satellite-5g Networks With Non-orthogonal Multiple Access, 2017 (01/09/2018-31/08/2021) - Lei Lei
Funders :
CE - Commission Européenne
European Union
Available on ORBilu :
since 03 December 2019

Statistics


Number of views
192 (31 by Unilu)
Number of downloads
348 (17 by Unilu)

Scopus citations®
 
19
Scopus citations®
without self-citations
19

Bibliography


Similar publications



Contact ORBilu