Sparse Underwater Acoustic Channels Research Articles

Dual parameters optimization [formula omitted]-LMS for estimating underwater acoustic channel with uncertain sparsity

The sparse norm constraint (l0,l1,l2 and lp) least mean square algorithm (LMS) is established technique for modeling sparse systems. However, when applied in target systems with uncertain sparsity, such as temporal-spatial-varying sparse underwater acoustic (UWA) channel, the parameters tuning (the step-size and parameter p) of lp-LMS faces significant challenges. In this paper, with the purpose to simplify the complicated dual-parameter selection problem via gradient strategy, a dual parameters optimization lp-LMS (DPO-lp-LMS) algorithm is derived by iteratively adjusting the step-size and the parameters p in parallel along the descent gradient. Convergence analysis of the proposed algorithm is given. A numerical simulation under varying sparsity systems exhibits that the proposed algorithm outperforms the lp-LMS algorithms driven by the existing optimization approaches in convergence speed and steady-state error. Meanwhile, a field shallow water experiment of UWA communication demonstrated that the proposed algorithm achieves superior performance under the framework of direct adaptation turbo equalization (DA-TEQ).

Adaptive identification of sparse underwater acoustic channels with a mix of static and time-varying parameters

We consider identification of sparse linear systems with a mix of static and time-varying parameters. Such systems are typical in underwater acoustics (UWA), for instance, in applications requiring identification of the acoustic channel, such as UWA communications, navigation and continuous-wave sonar. The recently proposed fast local basis function (fLBF) algorithm provides high performance when identifying time-varying systems. In this paper, we further improve the performance of the fLBF algorithm by exploiting properties of the system. Specifically, we propose an adaptive time-invariance test to identify whether a particular system tap is static or time-varying and exploit this knowledge for choosing the number of basis functions. We also propose a regularization scheme that exploits the system sparsity and an adaptive technique for estimating the regularization parameter. Finally, a debiasing technique is proposed to reduce an inherent bias of fLBF estimates. The high performance of the fLBF algorithm with the proposed techniques is demonstrated in scenarios of UWA communications, using numerical and real experiments.

Inter-carrier interference-aware sparse time-varying underwater acoustic channel estimation based on fast reconstruction algorithm

In this paper, a fast orthogonal matching pursuit (OMP) algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic (UWA) channel estimation. The channel estimation consists of calculating amplitude, delay and Doppler scaling factor of each path using the received multi-path signal. This algorithm, called as OIP-FOMP, can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels. In this algorithm, repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance. Efficient QR decomposition is used to estimate the path amplitude, and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix. Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm, without any loss of accuracy.

A Bayesian Receiver for Semiblind Equalization in Sparse Underwater Acoustic Channels Using Bernoulli Priors

Underwater acoustic channels are characterized by sparse channel structures, where very few significantly strong non-zero taps exist out of long delay spreads. Though some existing works typically have attempted to take advantage of the sparsity of multipath channels, substantial performance improvement remains elusive. In this work, we develop an iterative Markov chain Monte Carlo (MCMC) algorithm based on Gibbs sampling designed for sparse channels. We decompose the sparse channel response into components of sparsity pattern and sparse coefficient, and incorporate the sparse structure of channels in Bernoulli prior probability distribution for sparsity pattern. We derive the posterior distributions of both sparsity pattern and sparse coefficient components, thereby sampling of sparse channels could be obtained. Furthermore, our proposed algorithm is also generalizable to time-varying underwater acoustic channels. Numerical results are provided to demonstrate performance of our proposed algorithm.

Group sparse underwater acoustic channel estimation with impulsive noise: Simulation results based on Arctic ice cracking noise.

Many underwater acoustic (UWA) channels exhibit impulsive noise, thereby severely degrading the performance of traditional channel estimation algorithms. This paper presents two channel estimation algorithms for impulsive noise, namely (i) the variable forgetting factor l1,0 recursive least sign algorithm (VFF-l1,0-RLSA) and (ii) the variable forgetting factor l2,0 recursive least sign algorithm (VFF-l2,0-RLSA), both of which exploit the group sparse multipath structure and maintain robustness under impulsive noise. By using the l1 norm of the estimation error as part of the cost function, RLSAs are better at detecting and rejecting impulsive noise than the recursive least squares algorithms. A mixed l1,0 or l2,0 norm is incorporated with a RLSA to achieve better performance in group sparse UWA channel estimation. The time-varying forgetting factor and regularization parameter in the two proposed algorithms help to improve their performance. Simulation results based on Arctic ice cracking noise demonstrate the robustness and superiority of the two proposed algorithms.

A new joint channel equalization and estimation algorithm for underwater acoustic channels

Underwater acoustic channel (UAC) is one of the most challenging communication channels in the world, owing to its complex multi-path and absorption as well as variable ambient noise. Although adaptive equalization could effectively eliminate the inter-symbol interference (ISI) with the help of training sequences, the convergence rate of equalization in sparse UAC decreased remarkably. Besides, channel estimation algorithms could roughly figure out channel impulse response and other channel parameters through several specific mathematical criterions. In this paper, a typical channel estimation method, least square (LS) algorithm, is applied in adaptive equalization to obtain the initial tap weights of least mean square (LMS) algorithm. Simulation results show that the proposed method significantly enhances the convergence rate of the LMS algorithm.

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

This study proposes a novel approach to joint channel estimation and detection of orthogonal frequency division multiplexing transmission over underwater acoustic (UWA) multipath channels exhibiting cluster sparsity. Unlike most sparse channel estimations, the authors exploit the cluster-sparsity characteristic of UWA channels without additional prior information. They adopt a modified spike-and-slab prior model in their non-parametric Bayesian learning framework. To avoid the need for a closed-form Bayesian estimate, they apply the Markov chain Monte Carlo technique to joint achieve channel estimation and signal detection. The proposed solution is amenable to being integrated with soft-input soft-output decoding to improve the performance through turbo iteration. Simulation results demonstrate improved bit error rate of the proposed algorithm over existing algorithms.

A family of sparse group Lasso RLS algorithms with adaptive regularization parameters for adaptive decision feedback equalizer in the underwater acoustic communication system

In this paper, we propose a family of sparse group Lasso (least absolute shrinkage and selection operator) Recursive Least Squares (RLS) algorithms for sparse underwater acoustic channel equalization. The proposed adaptive RLS algorithms employ a family of mixed norms, such as l1l2,1-norm, l1l∞,1-norm, l1l2,0-norm, l1l1,0-norm, l0l2,1-norm, l0l∞,1-norm, l0l2,0-norm, and l0l1,0-norm, as the sparsity constraint in the penalty function to exploit the sparsity of the underwater acoustic communication system. The proposed adaptive RLS algorithms can adaptively select the regularization parameters regardless of whether the channel of underwater acoustic channel is general sparse channel, group sparse channel or the mixed sparse channel consisting of general sparse channel and group sparse channel. Moreover, this paper presents a direct adaptive decision feedback equalizer (DA-DFE) that exploits any sparse channel structure with the proposed adaptive RLS algorithms in the lake and sea experiments. Experimental results verify that the DA-DFE receiver with the proposed family of sparse group Lasso RLS algorithms can achieve a better performance in terms of convergence rate, mean square deviation (MSD) and symbol error rate (SER) in the single-input single-output (SISO) single carrier underwater acoustic communication system.

Optimized Decision Feedback Equalizer Algorithm based on Sparse Underwater Acoustic Channel

Optimized Decision Feedback Equalizer Algorithm based on Sparse Underwater Acoustic Channel

Sparse Channel Estimation and Equalization for OFDM-Based Underwater Cooperative Systems With Amplify-and-Forward Relaying

This paper is concerned with a challenging problem of channel estimation and equalization for amplify-and-forward cooperative relay based orthogonal frequency division multiplexing (OFDM) systems in sparse underwater acoustic (UWA) channels. The sparseness of the channel impulse response and prior information for the non-Gaussian channel gains, modeled by an exact continuous Gaussian mixture (CGM), are exploited to improve the performance of the channel estimation algorithm. The resulting novel algorithm initially estimates the overall sparse complex-valued channel taps from the source to the destination as well as their locations using the matching pursuit (MP) approach. The effective time-domain non-Gaussian noise is approximated well as a Gaussian noise in the frequency-domain, where the estimation takes place. An efficient and low complexity algorithm is developed based on a combination of the MP and the maximum a posteriori probability (MAP) based space-alternating generalized expectation-maximization technique, to improve the estimates of the channel taps and their locations in an iterative manner. Computer simulations show that the UWA channel is estimated very effectively and the proposed algorithm exhibits excellent symbol error rate and channel estimation performance.

An MMSE Approach to Channel Shorting for Underwater Acoustic FH-FSK Communication

Underwater acoustic channel is the biggest obstacle which causes the underwater acoustic communication falling behind the wireless communication. Generally speaking, underwater acoustic channel will represent doubly spread, i.e. multipath spread and Doppler spread. Also the multipath spread will be quite long in the underwater acoustic channel. For example, the multipath spread will be several milliseconds in the shallow sea and several seconds in the deep sea. Multipath spread will lead to Inter-Symbol Interference (ISI), and Doppler spread will cause the channel to be rapidly time-varying. Although ISI can be suppressed by channel equalizer, but because of the long multipath spread, it increases the complexity about the design of the equalizer. In this paper, channel shortening will be used to reduce the ISI. We present two methods of channel shorting, which are Time Reversal (TR) filter and the Minimum Mean Squared Error (MMSE) filter. When the two filters are used in FH-FSK, we found that the performance of MMSE filter is better than TR filter. Taking into account the characteristics of sparse underwater acoustic channel, Compressive Sensing (CS) algorithm is used for channel estimation. Finally, We perform a series of numerical simulations and the experiments on the lake to show that MMSE filter outperformances the TR filter under the FH-FSK system using the bit error rate (BER) as an evaluation criterion.

Sparse Underwater Acoustic Channel Parameter Estimation Using a Wideband Receiver Array

This paper considers a channel parameter estimation problem using a wideband multichannel receiver array. We estimate the sparse underwater acoustic communication channel parameters, such as time-delay, incidence angle, Doppler frequency, and complex amplitude of impinging multipath components. Multichannel signals from a receiver array are modeled to obtain additional sparsity in the incidence angle and the angle-delay-Doppler-spread function is defined to parameterize the channel. To estimate the principal entries of the angle-delay-Doppler-spread function, we propose a modified version of the orthogonal matching pursuit (OMP) algorithm to utilize a redundant dictionary. We introduce a space-alternating scheme which divides the entire parameter search space into smaller subsets to avoid handling large parameter search space. The performance of the proposed method is evaluated using two experimental data: one from large-scale water tank with the capability of generating surface gravity wave and the other from shallow sea water which shows sparse channel structure. Our results demonstrate that the proposed method accurately estimates the time-varying multipath channel parameters with lower residual error than the OMP.

New Sparse Adaptive Algorithms Based on the Natural Gradient and the ${L}_{0}$-Norm

A new algorithmic framework for sparse channel identification is proposed. Although the focus of this paper is on sparse underwater acoustic channels, this framework can be applied in any field where sequential noisy signal samples are obtained from a linear time-varying system. A suit of new algorithms is derived by minimizing a differentiable cost function that utilizes the underlying Riemannian structure of the channel as well as the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm of the complex-valued channel taps. The sparseness effect of the proposed algorithms is successfully demonstrated by estimating a mobile shallow-water acoustic channel. The clear superiority of the new algorithms over state-of-the-art sparse adaptive algorithms is shown. Moreover, the proposed algorithms are employed by a channel-estimate-based decision-feedback equalizer (CEB DFE). These CEB DFE structures are compared with a direct-adaptation DFE (DA DFE), which is based on sparse and nonsparse adaptation. Our results confirm the improved error-rate performance of the new CEB DFEs when the channel is sparse.

A New Blind Equalization Algorithm Suitable for Sparse Underwater Acoustic Channel

The Constant Modulus Algorithms is a mature algorithm for a long time. The researchers are fond of its solidity. But in the area of shallow sea or blue sea, the CMA has poor constringency. In this passage, based on a improved blind equalization algorithm, we proposed a new algorithm which suitable for sparse underwater acoustic channel. The computer simulation results demonstrate fast convergence and fewer accounts.

Deconvolution of sparse underwater acoustic multipath channel with a large time-delay spread

The deconvolution of multipath underwater acoustic channel with a large time-delay spread is investigated. The channel deconvolution involves estimating the multipath time-delays and attenuation factors from a noisy received signal consisting of multiple overlapped signals. Similar to conventional deconvolution methods, the proposed method estimates channel impulse response based on least-squares criterion. However, the proposed method harnesses the sparse structure of an underwater acoustic channel, and [script-l](1)-norm of the channel impulse response is adopted as the cost function to be minimized. In addition, the available a priori knowledge of support constraint and attenuation factor constraint are imposed and channel deconvolution problem is converted to a convex optimization problem. Instead of employing the existing standard algorithms, which require huge storage space and high computational complexity, a simple iterative algorithm for solving the optimization problem with fast convergence rate and low complexity is developed. The computational complexity of the proposed algorithm is O(N log(2)(N)) per iteration with N being the length of the received signal. Simulation results confirm that the proposed method provides better performance in terms of temporal resolution and robustness to noise compared with other extant multipath channel deconvolution techniques.

Sparse underwater acoustic OFDM channel estimation based on superimposed training

A superimposed training (ST) based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing (OFDM) channel while improving bandwidth transmission efficiency. A periodic low power training sequence is superimposed on the information sequence at the transmitter. The channel parameters can be estimated without consuming any extra system bandwidth, but an unknown information sequence can interfere with the ST channel estimation method, so in this paper, an iterative method was adopted to improve estimation performance. An underwater acoustic channel’s properties include large channel dimensions and a sparse structure, so a matching pursuit (MP) algorithm was used to estimate the nonzero taps, allowing the performance loss caused by additive white Gaussian noise (AWGN) to be reduced. The results of computer simulations show that the proposed method has good channel estimation performance and can reduce the peak-to-average ratio of the OFDM channel as well.