Signal processing for underwater acoustic communications

Singer, Andrew C.; Nelson, Jill K.; Kozat, Süleyman S.

doi:10.1109/mcom.2009.4752683

Cited by 231 publications

(126 citation statements)

References 14 publications

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“…This method is the basis of turbo equalization and is based on decoding methods for turbo codes [10]- [12] and has close connections to the iterative algorithms used in conjunction with so-called factor graphs [13]- [16]. This approach has been adapted to various communication tasks, ranging from detection and decoding of trellis coded modulation (TCM) [17], [18] and code division multiple access (CDMA) [19] to underwater acoustic communications [20]- [23] and optical fiber communications [24]- [26]. Over the last decade and a half, this joint, iterative approach has been applied to a vast array of problems that involve the use of an error control code to aid in the processing of another related task in the receive chain of a digital communication link.…”

Section: Turbo Equalization Overviewmentioning

confidence: 99%

“…The coefficient vector and the term are in this case given by and where is the power of the ISI channel. The estimates are given by (20), at the bottom of the page. The estimation error is given by…”

Section: E Siso Equalization Based On Linear Mmse Estimationmentioning

confidence: 99%

“…The coefficient vector is given by under this condition, which is the matched filter to the ISI channel impulse response already derived in (20). Performing turbo equalization based on this equalization algorithm has a severe convergence problem, i.e., large SNR is required to achieve performance improvements over the iterations [29].…”

Section: Approximate Linear Mmse Estimation Of Bpsk-modulated Symbolsmentioning

confidence: 99%

See 2 more Smart Citations

Turbo Equalization: An Overview

Tüchler

Singer

2011

IEEE Trans. Inform. Theory

177

160

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Abstract-Turbo codes and the iterative algorithm for decoding them sparked a new era in the theory and practice of error control codes. Turbo equalization followed as a natural extension to this development, as an iterative technique for detection and decoding of data that has been both protected with forward error correction and transmitted over a channel with intersymbol interference (ISI). In this paper, we review the turbo equalization approach to coded data transmission over ISI channels, with an emphasis on the basic ideas, some of the practical details, and many of the research directions that have arisen from this offshoot, introduced by Douillard, et al. of the original turbo decoding algorithm. The subsequent relaxation of the maximum a posteriori (MAP) equalization algorithm to include linear and other simpler receivers sparked a decade and a half of research into iterative algorithms, spanning research problems ranging from trellis coded modulation to underwater acoustic communications.Index Terms-Equalization, iterative decoding, minimum mean square error, turbo equalization.

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Section: Turbo Equalization Overviewmentioning

confidence: 99%

Section: E Siso Equalization Based On Linear Mmse Estimationmentioning

confidence: 99%

Section: Approximate Linear Mmse Estimation Of Bpsk-modulated Symbolsmentioning

confidence: 99%

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Turbo Equalization: An Overview

Tüchler

Singer

2011

IEEE Trans. Inform. Theory

177

160

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“…Underwater acoustic (UWA) channels pose grand challenges for reliable high data-rate communications, due to significant Doppler effects and severe delay spread [1][2][3]. In underwater communications, acoustic waves propagate at 1500 m/s, much lower than the speed of electromagnetic waves in terrestrial wireless systems (which is 3 × 10 8 m/s).…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for the Estimation of Doubly Spread Acoustic Channels Based on Wavelet Transform

Zhang

Song

et al. 2018

Applied Sciences

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Abstract:In this paper, the estimation of doubly spread underwater acoustic (UWA) channels is investigated. The UWA channels are characterized by severe delay spread and significant Doppler effects, and can be well modeled as a multi-scale multi-lag (MSML) channel. Furthermore, exploiting the sparsity of UWA channels, MSML channel estimation can be transformed into the estimation of parameter sets (amplitude, Doppler scale factor, time delay). Based on this, the orthogonal matching pursuit (OMP) algorithm has been widely used. However, the estimation accuracy of OMP depends on the size of the dictionary, which is related with both delay spread and Doppler spread. Thus it requires high computational complexity. This paper proposes a new method, called wavelet transform (WT) based algorithm, for the UWA channel estimation. Different from OMP algorithm which needs to search in both time domain and Doppler domain, WT-based algorithm only needs to search in time domain by using the Doppler invariant characteristic of hyperbolic frequency modulation (HFM) signal. The performance of the proposed algorithm is evaluated by computer simulations based on BELLHOP. The simulation results show that WT-based algorithm performs slightly better than OMP algorithm in low signal to noise ratio (SNR) while can greatly reduce computational complexity.

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“…Underwater acoustic (UWA) channels pose unique challenges due to the low speed of sound, limited communication bandwidth, and the intrinsic motion due to waves and currents [1]. Such channels feature large delay spread, frequency-dependent Doppler shift, and high time variability.…”

Section: Introductionmentioning

confidence: 99%

Markov Chain Monte Carlo detection for underwater acoustic channels

Wan

Chen

Choi

et al. 2010

2010 Information Theory and Applications Workshop (ITA)

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Abstract-In this work, we develop novel statistical detectors to combat intersymbol interference for frequency selective channels based on Markov Chain Monte Carlo (MCMC) techniques. While the optimal maximum a posteriori (MAP) detector has a complexity that grows exponentially with the constellation size and the memory of the channel, the MCMC detector can achieve near optimal performance with a complexity that grows linearly. This makes the MCMC detector particularly attractive for underwater acoustic channels with long delay spread. We examine the effectiveness of the MCMC detector using actual data collected from underwater experiments. When combined with adaptive least mean square (LMS) channel estimation, the MCMC detector achieves superior performance over the direct adaptation LMS turbo equalizers (LMS-TEQ) for a majority of data sets transmitted over distances from 60 meters to 1000 meters.

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Signal processing for underwater acoustic communications

Cited by 231 publications

References 14 publications

Turbo Equalization: An Overview

Turbo Equalization: An Overview

A Novel Approach for the Estimation of Doubly Spread Acoustic Channels Based on Wavelet Transform

Markov Chain Monte Carlo detection for underwater acoustic channels

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