Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams
Joint Authors
Yang, Xi
Xie, Xiaojuan
Peng, Shengliang
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Telecommunications Engineering
Abstract EN
Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission.
Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low.
This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams.
Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL.
Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation.
Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.
American Psychological Association (APA)
Xie, Xiaojuan& Peng, Shengliang& Yang, Xi. 2020. Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1192522
Modern Language Association (MLA)
Xie, Xiaojuan…[et al.]. Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams. Mobile Information Systems No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1192522
American Medical Association (AMA)
Xie, Xiaojuan& Peng, Shengliang& Yang, Xi. Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1192522
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192522