Robust sparse underwater acoustic channel estimation method via projected ℓ1-ℓ2 Optimization

Yongwei Wang; Yixin Yang; Lingji Xu; Xiaohui Bai

doi:10.1109/OCEANS-Genova.2015.7271646

Robust sparse underwater acoustic channel estimation method via projected ℓ₁-ℓ₂ Optimization

Yongwei Wang
, Yixin Yang
, Lingji Xu
, Xiaohui Bai

School of Marine Science and Technology

Northwestern Polytechnical University Xian

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

Abstract

By exploiting the intrinsic sparse structure of the underwater acoustic channel, we adopt ℓ_l-ℓ₂ optimization criterion, which incorporates least squares with the penalized ℓ₁ minimization to reduce noise effects while inducing channel sparsity. However, the estimate of channel parameters might not be in the feasible region. Therefore, we then define convex sets and utilize convex projection as a supplement to ℓ₁-ℓ₂ optimization method to further improve the estimation accuracy. In addition, we elaborate matrix multiplications of the ℓ₁-ℓ₂ solution into vector computation efficiently implemented by FFT. Simulation results show that the proposed method outperforms some conventional channel estimation techniques in low SNR scenarios, and it can resolve multipath effectively even when SNR equals -5dB.

Original language	English
Title of host publication	MTS/IEEE OCEANS 2015 - Genova
Subtitle of host publication	Discovering Sustainable Ocean Energy for a New World
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479987368
DOIs	https://doi.org/10.1109/OCEANS-Genova.2015.7271646
State	Published - 17 Sep 2015
Event	MTS/IEEE OCEANS 2015 - Genova - Genova, Italy Duration: 18 May 2015 → 21 May 2015

Publication series

Name	MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World

Conference

Conference	MTS/IEEE OCEANS 2015 - Genova
Country/Territory	Italy
City	Genova
Period	18/05/15 → 21/05/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

convex projection
low SNR
projected ℓ-ℓ optimization
sparse channel estimation

Access to Document

10.1109/OCEANS-Genova.2015.7271646

Cite this

Wang, Y., Yang, Y., Xu, L., & Bai, X. (2015). Robust sparse underwater acoustic channel estimation method via projected ℓ₁-ℓ₂ Optimization. In MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World Article 7271646 (MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/OCEANS-Genova.2015.7271646

Wang, Yongwei ; Yang, Yixin ; Xu, Lingji et al. / Robust sparse underwater acoustic channel estimation method via projected ℓ₁-ℓ₂ Optimization. MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World. Institute of Electrical and Electronics Engineers Inc., 2015. (MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World).

@inproceedings{9dc9349fba7b416e86e245af7ffaaac4,

title = "Robust sparse underwater acoustic channel estimation method via projected ℓ1-ℓ2 Optimization",

abstract = "By exploiting the intrinsic sparse structure of the underwater acoustic channel, we adopt ℓl-ℓ2 optimization criterion, which incorporates least squares with the penalized ℓ1 minimization to reduce noise effects while inducing channel sparsity. However, the estimate of channel parameters might not be in the feasible region. Therefore, we then define convex sets and utilize convex projection as a supplement to ℓ1-ℓ2 optimization method to further improve the estimation accuracy. In addition, we elaborate matrix multiplications of the ℓ1-ℓ2 solution into vector computation efficiently implemented by FFT. Simulation results show that the proposed method outperforms some conventional channel estimation techniques in low SNR scenarios, and it can resolve multipath effectively even when SNR equals -5dB.",

keywords = "convex projection, low SNR, projected ℓ-ℓ optimization, sparse channel estimation",

author = "Yongwei Wang and Yixin Yang and Lingji Xu and Xiaohui Bai",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; MTS/IEEE OCEANS 2015 - Genova ; Conference date: 18-05-2015 Through 21-05-2015",

year = "2015",

month = sep,

day = "17",

doi = "10.1109/OCEANS-Genova.2015.7271646",

language = "英语",

series = "MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "MTS/IEEE OCEANS 2015 - Genova",

}

Wang, Y, Yang, Y, Xu, L & Bai, X 2015, Robust sparse underwater acoustic channel estimation method via projected ℓ₁-ℓ₂ Optimization. in MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World., 7271646, MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World, Institute of Electrical and Electronics Engineers Inc., MTS/IEEE OCEANS 2015 - Genova, Genova, Italy, 18/05/15. https://doi.org/10.1109/OCEANS-Genova.2015.7271646

Robust sparse underwater acoustic channel estimation method via projected ℓ₁-ℓ₂ Optimization. / Wang, Yongwei; Yang, Yixin; Xu, Lingji et al.
MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World. Institute of Electrical and Electronics Engineers Inc., 2015. 7271646 (MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World).

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

TY - GEN

T1 - Robust sparse underwater acoustic channel estimation method via projected ℓ1-ℓ2 Optimization

AU - Wang, Yongwei

AU - Yang, Yixin

AU - Xu, Lingji

AU - Bai, Xiaohui

PY - 2015/9/17

Y1 - 2015/9/17

N2 - By exploiting the intrinsic sparse structure of the underwater acoustic channel, we adopt ℓl-ℓ2 optimization criterion, which incorporates least squares with the penalized ℓ1 minimization to reduce noise effects while inducing channel sparsity. However, the estimate of channel parameters might not be in the feasible region. Therefore, we then define convex sets and utilize convex projection as a supplement to ℓ1-ℓ2 optimization method to further improve the estimation accuracy. In addition, we elaborate matrix multiplications of the ℓ1-ℓ2 solution into vector computation efficiently implemented by FFT. Simulation results show that the proposed method outperforms some conventional channel estimation techniques in low SNR scenarios, and it can resolve multipath effectively even when SNR equals -5dB.

AB - By exploiting the intrinsic sparse structure of the underwater acoustic channel, we adopt ℓl-ℓ2 optimization criterion, which incorporates least squares with the penalized ℓ1 minimization to reduce noise effects while inducing channel sparsity. However, the estimate of channel parameters might not be in the feasible region. Therefore, we then define convex sets and utilize convex projection as a supplement to ℓ1-ℓ2 optimization method to further improve the estimation accuracy. In addition, we elaborate matrix multiplications of the ℓ1-ℓ2 solution into vector computation efficiently implemented by FFT. Simulation results show that the proposed method outperforms some conventional channel estimation techniques in low SNR scenarios, and it can resolve multipath effectively even when SNR equals -5dB.

KW - convex projection

KW - low SNR

KW - projected ℓ-ℓ optimization

KW - sparse channel estimation

UR - https://www.scopus.com/pages/publications/84957617423

U2 - 10.1109/OCEANS-Genova.2015.7271646

DO - 10.1109/OCEANS-Genova.2015.7271646

M3 - 会议稿件

AN - SCOPUS:84957617423

T3 - MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World

BT - MTS/IEEE OCEANS 2015 - Genova

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - MTS/IEEE OCEANS 2015 - Genova

Y2 - 18 May 2015 through 21 May 2015

ER -

Wang Y, Yang Y, Xu L, Bai X. Robust sparse underwater acoustic channel estimation method via projected ℓ₁-ℓ₂ Optimization. In MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World. Institute of Electrical and Electronics Engineers Inc. 2015. 7271646. (MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World). doi: 10.1109/OCEANS-Genova.2015.7271646