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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Bibliographic Details
Published in:IEEE communications letters Vol. 24; no. 8; pp. 1808 - 1811
Main Authors: Huang, Lihuan, Zhang, Qunfei, Zhang, Lifan, Shi, Juan, Zhang, Lingling
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
adaptive modulation and coding (AMC)
Adaptive systems
Channel estimation
Coding
Feature extraction
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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
Online Access:Get full text
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Summary: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.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2020.2990188