Estimation of underwater acoustic channel via block-sparse recursive least-squares algorithm

Tian Tian; Fei Yun Wu; Kunde Yang

doi:10.1109/ICSPCC46631.2019.8960886

Estimation of underwater acoustic channel via block-sparse recursive least-squares algorithm

Tian Tian, Fei Yun Wu, Kunde Yang

Ocean Institute

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Underwater acoustic communication suffers from the serious multipath effect and fast time variation due to the sophisticated ocean environment. General channel estimation schemes have limited performance in underwater acoustic communication because of the lacking exploitation of the underwater acoustic channel (UAC) inherent property. In this work, we address a new approximate mixed ℓ_2,0-norm, and based on that norm, we develop a block-sparse recursive least-squares (BS-RLS) algorithm for UAC estimation which takes advantage of the underlying block-sparse structure of UAC. The simulation result shows that the proposed BS-RLS algorithm improves the channel estimation quality under block-sparse condition.

Original language	English
Title of host publication	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728117072
DOIs	https://doi.org/10.1109/ICSPCC46631.2019.8960886
State	Published - Sep 2019
Event	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019 - Dalian, Liaoning, China Duration: 20 Sep 2019 → 22 Sep 2019

Publication series

Name	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

Conference

Conference	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
Country/Territory	China
City	Dalian, Liaoning
Period	20/09/19 → 22/09/19

Keywords

Block sparse
Channel estimation
Mixed norm constraint
Recursive least squares (RLS)
Underwater acoustic channel (UAC)

Access to Document

10.1109/ICSPCC46631.2019.8960886

Cite this

Tian, T., Wu, F. Y., & Yang, K. (2019). Estimation of underwater acoustic channel via block-sparse recursive least-squares algorithm. In 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019 Article 8960886 (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSPCC46631.2019.8960886

Tian, Tian ; Wu, Fei Yun ; Yang, Kunde. / Estimation of underwater acoustic channel via block-sparse recursive least-squares algorithm. 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019).

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title = "Estimation of underwater acoustic channel via block-sparse recursive least-squares algorithm",

abstract = "Underwater acoustic communication suffers from the serious multipath effect and fast time variation due to the sophisticated ocean environment. General channel estimation schemes have limited performance in underwater acoustic communication because of the lacking exploitation of the underwater acoustic channel (UAC) inherent property. In this work, we address a new approximate mixed ℓ2,0-norm, and based on that norm, we develop a block-sparse recursive least-squares (BS-RLS) algorithm for UAC estimation which takes advantage of the underlying block-sparse structure of UAC. The simulation result shows that the proposed BS-RLS algorithm improves the channel estimation quality under block-sparse condition.",

keywords = "Block sparse, Channel estimation, Mixed norm constraint, Recursive least squares (RLS), Underwater acoustic channel (UAC)",

author = "Tian Tian and Wu, {Fei Yun} and Kunde Yang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019 ; Conference date: 20-09-2019 Through 22-09-2019",

year = "2019",

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language = "英语",

series = "2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019",

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Tian, T, Wu, FY & Yang, K 2019, Estimation of underwater acoustic channel via block-sparse recursive least-squares algorithm. in 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019., 8960886, 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019, Dalian, Liaoning, China, 20/09/19. https://doi.org/10.1109/ICSPCC46631.2019.8960886

Estimation of underwater acoustic channel via block-sparse recursive least-squares algorithm. / Tian, Tian; Wu, Fei Yun; Yang, Kunde.
2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8960886 (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Estimation of underwater acoustic channel via block-sparse recursive least-squares algorithm

AU - Tian, Tian

AU - Wu, Fei Yun

AU - Yang, Kunde

PY - 2019/9

Y1 - 2019/9

N2 - Underwater acoustic communication suffers from the serious multipath effect and fast time variation due to the sophisticated ocean environment. General channel estimation schemes have limited performance in underwater acoustic communication because of the lacking exploitation of the underwater acoustic channel (UAC) inherent property. In this work, we address a new approximate mixed ℓ2,0-norm, and based on that norm, we develop a block-sparse recursive least-squares (BS-RLS) algorithm for UAC estimation which takes advantage of the underlying block-sparse structure of UAC. The simulation result shows that the proposed BS-RLS algorithm improves the channel estimation quality under block-sparse condition.

AB - Underwater acoustic communication suffers from the serious multipath effect and fast time variation due to the sophisticated ocean environment. General channel estimation schemes have limited performance in underwater acoustic communication because of the lacking exploitation of the underwater acoustic channel (UAC) inherent property. In this work, we address a new approximate mixed ℓ2,0-norm, and based on that norm, we develop a block-sparse recursive least-squares (BS-RLS) algorithm for UAC estimation which takes advantage of the underlying block-sparse structure of UAC. The simulation result shows that the proposed BS-RLS algorithm improves the channel estimation quality under block-sparse condition.

KW - Block sparse

KW - Channel estimation

KW - Mixed norm constraint

KW - Recursive least squares (RLS)

KW - Underwater acoustic channel (UAC)

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BT - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

Y2 - 20 September 2019 through 22 September 2019

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Tian T, Wu FY, Yang K. Estimation of underwater acoustic channel via block-sparse recursive least-squares algorithm. In 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8960886. (2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019). doi: 10.1109/ICSPCC46631.2019.8960886

Estimation of underwater acoustic channel via block-sparse recursive least-squares algorithm

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