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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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
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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Abstract 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.
AbstractList 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.
Author Zhang, Lifan
Zhang, Qunfei
Shi, Juan
Huang, Lihuan
Zhang, Lingling
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10.1109/ACCESS.2019.2908768
10.1109/EW.2010.5483527
10.1111/j.1467-9868.2005.00503.x
10.1109/LCOMM.2018.2792015
10.1109/MCOM.2009.4752682
10.1155/2019/6470359
10.1109/JPROC.2018.2846588
10.3390/s17071477
10.1198/106186006X113430
10.1007/11494669_93
10.1109/JOE.2018.2883872
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  article-title: A two-dimensional strategy of adaptive modulation and coding for underwater acoustic communication systems
  publication-title: Proc IEEE Int Conf Signal Inf Data Process (ICSIDP)
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SubjectTerms 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)
Title Efficiency Enhancement for Underwater Adaptive Modulation and Coding Systems: Via Sparse Principal Component Analysis
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https://www.proquest.com/docview/2434120302
Volume 24
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