Efficiency Enhancement for Underwater Adaptive Modulation and Coding Systems: Via Sparse Principal Component Analysis

In this letter, to explore key channel state information (CSI) as a more efficient switching metric in the task of underwater adaptive modulation and coding (AMC), a sparse principal component analysis (SPCA) based approach is proposed from the perspective of statistical analysis plus machine learni...

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Veröffentlicht in:IEEE communications letters Jg. 24; H. 8; S. 1808 - 1811
Hauptverfasser: Huang, Lihuan, Zhang, Qunfei, Zhang, Lifan, Shi, Juan, Zhang, Lingling
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
adaptive modulation and coding (AMC)
Adaptive systems
Channel estimation
Coding
Feature extraction
Loading
Machine learning
Measurement
Modulation
Principal component analysis
Principal components analysis
sparse principal component analysis (SPCA)
Statistical analysis
Switches
Switching
Underwater
Underwater acoustic communication (UAC)
ISSN:1089-7798, 1558-2558
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Zusammenfassung:In this letter, to explore key channel state information (CSI) as a more efficient switching metric in the task of underwater adaptive modulation and coding (AMC), a sparse principal component analysis (SPCA) based approach is proposed from the perspective of statistical analysis plus machine learning (ML). This data-driven sparse learning method can offer significant system efficiency enhancement in the procedures of both channel estimation and communication scheme switching. By leveraging a dataset that contains real-world channel measurements collected from three field experiments, simulations demonstrate the effectiveness of the proposed scheme.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2020.2990188