The Rayleigh Fading Channel Prediction via Deep Learning
Joint Authors
Wu, Jinsong
Liao, Run-Fa
Song, Huanhuan
Pan, Fei
Dong, Lian
Wen, Hong
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-25
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Liao, Run-Fa& Wen, Hong& Wu, Jinsong& Song, Huanhuan& Pan, Fei& Dong, Lian. 2018. The Rayleigh Fading Channel Prediction via Deep Learning. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1216159
Modern Language Association (MLA)
Liao, Run-Fa…[et al.]. The Rayleigh Fading Channel Prediction via Deep Learning. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1216159
American Medical Association (AMA)
Liao, Run-Fa& Wen, Hong& Wu, Jinsong& Song, Huanhuan& Pan, Fei& Dong, Lian. The Rayleigh Fading Channel Prediction via Deep Learning. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1216159
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1216159