Denoising enabled channel estimation for underwater acoustic communications: A sparsity-aware model-driven learning approach

Denoising enabled channel estimation for underwater acoustic communications: A sparsity-aware model-driven learning approach

It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions, including frequency-selective property, high relative mobility, long propagation latency, and intensive ambient noise, etc. To t...

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Bibliographic Details
Main Authors: Sicong Liu, Younan Mou, Xianyao Wang, Danping Su, Ling Cheng
Format: Article
Language:English
Published: Tsinghua University Press 2023-03-01
Series:Intelligent and Converged Networks
Subjects:
orthogonal frequency division multiplexing (ofdm)
underwater acoustic communications (uac)
sparse recovery
deep learning
sparse learning
denoising
approximate message passing
Online Access:https://www.sciopen.com/article/10.23919/ICN.2023.0001

Internet

https://www.sciopen.com/article/10.23919/ICN.2023.0001

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