The rayleigh fading channel prediction via deep learning
Author
dc.contributor.author
Liao, Run-Fa
Author
dc.contributor.author
Wen, Hong
Author
dc.contributor.author
Wu, Jinsong
Author
dc.contributor.author
Song, Huanhuan
Author
dc.contributor.author
Pan, Fei
Author
dc.contributor.author
Dong, Lian
Admission date
dc.date.accessioned
2019-01-23T13:28:27Z
Available date
dc.date.available
2019-01-23T13:28:27Z
Publication date
dc.date.issued
2018
Cita de ítem
dc.identifier.citation
Wireless Communications and Mobile Computing Volume 2018, Article ID 6497340, 11 pages
es_ES
Identifier
dc.identifier.other
10.1155/2018/6497340
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/159538
Abstract
dc.description.abstract
This paper presents a multi-time channel prediction system based on backpropagation (BP) neural network with multi-hidden layers, which can predict channel information effectively and benefit for massive MIMO performance, power control, and artificial noise physical layer security scheme design. Meanwhile, an early stopping strategy to avoid the overfitting of BP neural network is introduced. By comparing the predicted normalized mean square error (NMSE), the simulation results show that the performances of the proposed scheme are extremely improved. Moreover, a sparse channel sample construction method is proposed, which saves system resources effectively without weakening performances.
es_ES
Patrocinador
dc.description.sponsorship
NSFC
61572114
National Major RD Program
2018YFB0904905
Chile Conicyt Fondecyt Project
1181809