Joint channel estimation and detection using Markov chain Monte Carlo method over sparse underwater acoustic channels

Jing, Lianyou; He, Chengbing; Huang, Jianguo; Ding, Zhi

doi:10.1049/iet-com.2016.1339

Cited by 14 publications

(4 citation statements)

References 45 publications

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“…These approaches can compensate a constant Doppler shift (see Case in Section 4). Other approaches for constant Doppler shift compensation (not all necessarily in the context of underwater communications) have been proposed by Sharif et al, Li et al, Kim et al, Ming et al, Huang et al, Amar et al, Jing et al, Li et al, Lu et al, Zhang et al, Yu et al, Hou et al, and Huang et al In our modem design (Sections 3 and 4), we address variable Doppler shifts, which can be linear and nonlinear. We have addressed the linear case in a previous work .…”

Section: Related Workmentioning

confidence: 99%

Tuning the demodulation frequency based on a normalized trajectory model for mobile underwater acoustic communications

Ahmad

Barbeau

García-Alfaro

et al. 2019

Trans Emerging Tel Tech

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We have developed a demodulator for low data rate, asynchronous frame, and narrow bandwidth underwater acoustic communication. We aim at operation under harsh conditions, ie, low signal-to-noise ratio, and across long distances.In this paper, we pay a special attention to the efficiency of mobility support.Mobility results into the Doppler effect, which, for a demodulator, makes the carrier frequency drift arbitrarily during attempts to decode frames. The chances of success are better when the demodulator can tune into the drifted carrier frequency. This can be achieved by trying a range of possible drifted carriers. We introduce the novel idea of normalized trajectory. Each normalized trajectory produces a unique Doppler shift pattern that can be applied to tune into a drifted carrier. We demonstrate that this improvement is theoretically sound. From a practical point of view, the search space is potentially reduced. The actual gain in performance is application-specific and depends on the actual sets of trajectory parameters that are considered. We introduce the concept of normalized trajectory, discuss its integration into the demodulator, and review the performance of the new design.Trans Emerging Tel Tech. 2019;30:e3712.wileyonlinelibrary.com/journal/ett

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Section: Related Workmentioning

confidence: 99%

Tuning the demodulation frequency based on a normalized trajectory model for mobile underwater acoustic communications

Ahmad

Barbeau

García-Alfaro

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…In the multi‐scale case, the estimation methods should include a repeated waveform rebuilt process. The parameters of each path are determined by the LS or highest degree of correlation between the reproduced waveform and the received signal [10–12]. Then, the received signal updates itself by eliminating the estimated path components.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation based on factor graph in wide‐band OFDM systems

Jiang

Xiang

2019

IET Communications

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“…As the dynamic propagation phenomena such as scattering from rough surfaces will cause diffuse multipath patterns, which can be properly modeled by a block sparse channel instead of a purely sparse one, the block-orthogonal matching pursuit (BOMP) algorithm has been proposed to address this type of dynamic sparsity. 23,24 As the Delay-Doppler spread function is capable of converting the time varying sparse structure at time domain into a more stable Delay-Doppler representation, 25 channel estimation approaches based on the Delay-Doppler spread function are attractive for estimating rapidly time varying channels. Previous investigations that addressed the optimization of Delay-Doppler channel model with OMP algorithm 25 or basis expansion models 26 have been reported.…”

Section: Introductionmentioning

confidence: 99%

“…First, by converting the estimation of time varying UWA channel into a DCS problem, the dynamic compressed sensing algorithm is designed for the UWA channel that simultaneously exhibits large delay spread and fast time variations. Second, based on numerical simulations, we provide performance comparisons among the OMP, smoothed l0 estimation (SL0), 12,13 BOMP, 23,24 linear Kalman estimation (Kalman), 31 Least Square QR-factorization (LSQR) 19 algorithm, as well as the proposed KF-CS algorithm. Furthermore, field data obtained from a physical shallow water channel is adopted to assess the impacts of different channel estimation algorithms on the UWA communication performance.…”

Section: Introductionmentioning

confidence: 99%

Exploiting time varying sparsity for underwater acoustic communication via dynamic compressed sensing

Jiang

Zheng

Zhou

et al. 2018

The Journal of the Acoustical Society of America

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While it has been recognized that the multipath structure of the underwater acoustic (UWA) channel offers the potential for compressed sensing (CS) sparsity exploitation, the rapidly time varying arrivals induced by highly dynamic surfaces unfortunately pose significant difficulties to channel estimation. From the viewpoint of underwater acoustic propagation, with the exception of the highly time varying arrivals caused by dynamic surface, generally there exist relatively stationary or slowly changing arrivals caused by direct path or bottom reflection, which imply the adoption of a discriminate estimation method to handle sparse components with different time variation scale. By modeling the time varying UWA channels as a sparse set consisting of constant and timevarying supports, in this paper, estimation of time varying UWA channel is transformed into a problem of dynamic compressed sensing sparse recovery. The combination of a Kalman filter and compressed sensing is adopted to pursue the solution of it. Numerical simulations demonstrate the superiority of the proposed algorithm. In the form of a channel-estimation-based decision-feedback equalizer, the experimental results with the field data obtained in a shallow water acoustic communication experiment indicate that the proposed dynamic compressed sensing algorithm outperforms classic algorithms as well as CS algorithms.

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Joint channel estimation and detection using Markov chain Monte Carlo method over sparse underwater acoustic channels

Cited by 14 publications

References 45 publications

Tuning the demodulation frequency based on a normalized trajectory model for mobile underwater acoustic communications

Tuning the demodulation frequency based on a normalized trajectory model for mobile underwater acoustic communications

Parameter estimation based on factor graph in wide‐band OFDM systems

Exploiting time varying sparsity for underwater acoustic communication via dynamic compressed sensing

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