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• W3019463670 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." @default.
• W3019463670 created "2020-05-01" @default.
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• W3019463670 date "2020-08-01" @default.
• W3019463670 modified "2023-10-16" @default.
• W3019463670 title "Efficiency Enhancement for Underwater Adaptive Modulation and Coding Systems: Via Sparse Principal Component Analysis" @default.
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• W3019463670 doi "https://doi.org/10.1109/lcomm.2020.2990188" @default.
• W3019463670 hasPublicationYear "2020" @default.
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