Estimation of Overspread Underwater Acoustic Channel Based on Low-Rank Matrix Recovery.

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Bibliographic Details
Title: Estimation of Overspread Underwater Acoustic Channel Based on Low-Rank Matrix Recovery.
Authors: Li, Jie, Chen, Fangjiong, Liu, Songzuo, Yu, Hua, Ji, Fei
Source: Sensors (14248220); Nov2019, Vol. 19 Issue 22, p4976-4976, 1p
Subject Terms: LOW-rank matrices, CHANNEL estimation, MULTICHANNEL communication, DISPERSION (Chemistry)
Abstract: In this paper, the estimation of overspread, i.e., doubly spread underwater acoustic (UWA) channels of strong dispersion is considered. We show that although the UWA channel dispersion causes the degeneration of channel sparsity, it leads to a low-rank structure especially when the channel delay-Doppler-spread function is separable in delay and Doppler domain. Therefore, we introduce the low-rank criterion to estimate the UWA channels, which can help to improve the estimation performance in the case of strong dispersion. The estimator is based on the discrete delay-Doppler-spread function representation of channel, and is formulated as a low-rank matrix recovery problem which can be solved by the singular value projection technique. Simulation examples are carried out to demonstrate the effectiveness of the proposed low-rank-based channel estimator. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:In this paper, the estimation of overspread, i.e., doubly spread underwater acoustic (UWA) channels of strong dispersion is considered. We show that although the UWA channel dispersion causes the degeneration of channel sparsity, it leads to a low-rank structure especially when the channel delay-Doppler-spread function is separable in delay and Doppler domain. Therefore, we introduce the low-rank criterion to estimate the UWA channels, which can help to improve the estimation performance in the case of strong dispersion. The estimator is based on the discrete delay-Doppler-spread function representation of channel, and is formulated as a low-rank matrix recovery problem which can be solved by the singular value projection technique. Simulation examples are carried out to demonstrate the effectiveness of the proposed low-rank-based channel estimator. [ABSTRACT FROM AUTHOR]
ISSN:14248220
DOI:10.3390/s19224976