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

Lihuan Huang; Qunfei Zhang; Lifan Zhang; Juan Shi; Lingling Zhang

doi:10.1109/LCOMM.2020.2990188

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

Lihuan Huang, Qunfei Zhang, Lifan Zhang, Juan Shi, Lingling Zhang

School of Marine Science and Technology

Northwestern Polytechnical University Xian

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

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.

Original language	English
Article number	9078098
Pages (from-to)	1808-1811
Number of pages	4
Journal	IEEE Communications Letters
Volume	24
Issue number	8
DOIs	https://doi.org/10.1109/LCOMM.2020.2990188
State	Published - Aug 2020

Keywords

adaptive modulation and coding (AMC)
sparse principal component analysis (SPCA)
Underwater acoustic communication (UAC)

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10.1109/LCOMM.2020.2990188

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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.",

keywords = "adaptive modulation and coding (AMC), sparse principal component analysis (SPCA), Underwater acoustic communication (UAC)",

author = "Lihuan Huang and Qunfei Zhang and Lifan Zhang and Juan Shi and Lingling Zhang",

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T2 - Via Sparse Principal Component Analysis

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AU - Zhang, Qunfei

AU - Zhang, Lifan

AU - Shi, Juan

AU - Zhang, Lingling

PY - 2020/8

Y1 - 2020/8

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AB - 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.

KW - adaptive modulation and coding (AMC)

KW - sparse principal component analysis (SPCA)

KW - Underwater acoustic communication (UAC)

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JO - IEEE Communications Letters

JF - IEEE Communications Letters

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M1 - 9078098

ER -

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

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