Adaptive Modulation and Coding Using Neural Network Based SNR Estimation

In this paper, we propose a novel Adaptive Modulation and Coding (AMC) scheme enabled by Artificial Neural Network (ANN) aided Signal-to-Noise power Ratio (SNR) estimation. The Power Spectral Density (PSD) values are trained for SNR classification and it is mapped to respective Modulation and Coding...

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Veröffentlicht in:IEEE access Jg. 7; S. 183545 - 183553
Hauptverfasser: Kojima, Shun, Maruta, Kazuki, Ahn, Chang-Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
adaptive modulation and coding
artificial neural network
Artificial neural networks
Coding
Doppler effect
Encoding
Estimation
Modulation
Neural networks
OFDM
Power spectral density
Receivers
Signal to noise ratio
SNR estimation
ISSN:2169-3536, 2169-3536
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Beschreibung
Zusammenfassung:In this paper, we propose a novel Adaptive Modulation and Coding (AMC) scheme enabled by Artificial Neural Network (ANN) aided Signal-to-Noise power Ratio (SNR) estimation. The Power Spectral Density (PSD) values are trained for SNR classification and it is mapped to respective Modulation and Coding Scheme (MCS) sets. Once trained, optimal MCS can be determined in low calculation complexity. The proposed approach is robust especially in high mobility environment since the PSD appearance is hardly influenced by the Doppler shift. Its effectiveness in terms of throughput is presented through computer simulations compared to the existing Error Vector Magnitude (EVM) based link adaptation scheme.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2946973