Acoustic Channel Estimation Research Articles

A TMSBL underwater acoustic channel estimation method based on dictionary learning denoising

The shallow sea underwater acoustic channel exhibits a significant sparse multipath structure. The temporally multiple sparse Bayesian learning (TMSBL) algorithm can effectively estimate this sparse multipath channel. However, the complexity of the algorithm is high, the signal-to-noise ratio (SNR) of shallow-sea underwater acoustic communication is low, and the estimation performance of the TMSBL algorithm is greatly affected by noise. To address this problem, an improved TMSBL underwater acoustic channel estimation method based on a dictionary learning noise reduction algorithm is proposed. Firstly, the K-Singular Value Decomposition (K-SVD) dictionary learning method is used to reduce the noise of the received pilot matrix, reducing the influence of noise on the signal. Then, the Generalized Orthogonal Matching Pursuit (GOMP) channel estimation method is combined to obtain a priori information such as the perceptual matrix and hyperparameter matrix for TMSBL channel estimation; and the noise variance is obtained by using the null subcarrier calculation instead of iteratively updating the noise variance in the TMSBL, to improve the estimation accuracy and reduce the algorithmic complexity. Finally, the TMSBL channel estimation method is used to estimate the underwater acoustic channels of different symbols jointly. The simulation results show that the normalized mean square error of the channel estimation of the improved TMSBL method is reduced by about 92.2% compared with the TMSBL algorithm, obtaining higher estimation accuracy; running time is reduced by about 45.6%, and there is also better performance in terms of the running speed, which provides a reference for underwater acoustic channel estimation.

Multi-state Markovian-random walk adaptive filter for time-varying block sparse system identification

This paper presents a higher order multi-state Markov model for block sparsity model of a system impulse response. To capture the time-varying behavior of the system, a random walk model for temporal evolution is incorporated; resulting in a two-dimensional multi-state Markov-Random walk model across spatial and temporal domains. Additionally, a block batch data structure is utilized for the adaptive filter design. The proposed adaptive filter is aimed to find the Maximum A Posteriori (MAP) estimator of the batch time-varying impulse response. The associated optimization problem is demonstrated to be convex and efficiently solvable through the Steepest-Descent (SD) approach. Furthermore, the final recursion of the proposed adaptive filter is derived and a mean convergence analysis of the algorithm is provided. Simulation results show the effectiveness of the proposed algorithm under severe time-varying conditions of the system impulse response, where alternative algorithms may exhibit divergence or poor convergence. Synthetic experiments and real-world experiments using acoustic channel estimation further validate the superiority of the proposed algorithm.

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm.

Channel estimation for underwater acoustic OFDM based on super‐resolution network

AbstractIn this letter, we propose a method for underwater acoustic channel estimation that combines image super‐resolution (SR) and is named FCDnNet. FCDnNet consists of two parts: Fast Super Resolution Convolutional Neural Network (FSRCNN) and Complex Denoising Convolutional Neural Network (C‐DnCNN). FSRCNN extracts effective features of pilot channels, uses deconvolution to achieve SR reconstruction, and generates a pre‐estimation channel matrix. C‐DnCNN preserves the relative positions of the real and imaginary parts of the channel, fully utilizing amplitude and phase information, and can more effectively recover the channel matrix from the pre‐estimation matrix. Experimental results show that the normalized mean square error (NMSE) of FCDnNet is at least 13.1–65.2 lower than other channel estimation methods based on deep learning.

Robust Underwater Acoustic Channel Estimation Method Based on Bias-Free Convolutional Neural Network

In recent years, the study of deep learning techniques for underwater acoustic channel estimation has gained widespread attention. However, existing neural network channel estimation methods often overfit to training dataset noise levels, leading to diminished performance when confronted with new noise levels. In this research, a “bias-free” denoising convolutional neural network (DnCNN) method is proposed for robust underwater acoustic channel estimation. The paper offers a theoretical justification for bias removal and customizes the fundamental DnCNN framework to give a specialized design for channel estimation, referred to as the bias-free complex DnCNN (BF-CDN). It uses least squares channel estimation results as input and employs a CNN model to learn channel characteristics and noise distribution. The proposed method effectively utilizes the temporal correlation inherent in underwater acoustic channels to further enhance estimation performance and robustness. This method adapts to varying noise levels in underwater environments. Experimental results show the robustness of the method under different noise conditions, indicating its potential to improve the accuracy and reliability of channel estimation.

Research on Distributed Compressed Sensing With Dynamic Block Sparsity for Underwater Acoustic Channel Estimation

Channel estimation plays a crucial role in Internet of Underwater Things networks. Traditional channel estimation algorithms have limited performance improvement under the block-sparsity underwater acoustic channel, and the distributed compressed sensing (DCS) method has not been researched under block-sparsity recovery. In this article, we propose DCS with block sparsity for underwater acoustic channel estimation under short observation length and low SNR scenarios. Specifically, the underwater acoustic channels are partitioned some sub-blocks that have the identical size, and the simultaneous block orthogonal matching pursuit algorithm (SBOMP) is proposed to enhance the channel estimates that have common delays. However, the number of nonzero taps located in a partitioned sub-block does not equal to the size of partitioned sub-blocks, and the channel between adjacent two data blocks may have slightly difference, when the SBOMP algorithm is applied, there would be much estimated noise. In order to address this problem, in this article, we also propose dynamic block sparsity-based SBOMP which is referred to as DSBOMP. The proposed DSBOMP consists of the SBOMP algorithm, first-order derivative of the residual method, and parallel comparison strategy. The first-order derivative is used to remove some negligible taps and dynamically measure the number of significant taps; the parallel comparison strategy is used to check whether the current taps are available. Both simulation and sea trial data indicate that, our proposed SBOMP and DSBOMP algorithms have better channel estimation performance than tradition algorithms under short observation length and low SNR. Moreover, our proposed DSBOMP achieves the best communication performance.

An Adaptive MP Algorithm for Underwater Acoustic Channel Estimation Based on Compressed Sensing

An Adaptive MP Algorithm for Underwater Acoustic Channel Estimation Based on Compressed Sensing

Modeling and channel estimation for piezo-acoustic backscatter assisted underwater acoustic communications

Relying on direct and converse piezoelectric effects, piezo-acoustic backscatter (PAB) technology reflects ambient acoustic signals to enable underwater backscatter communications at near-zero power, which was first realized through a prototype. In this paper, we propose a mathematical model of the PAB assisted underwater acoustic (UWA) communication, and address the sparse channel estimation problem. First, we present a five-stage backscatter process to derive the backscatter coefficient, and propose the channel model for the shallow-water communications. Then, we formulate the shallow-water acoustic channel estimation problem as a sparse vector recovery one according to the compressed sensing theory, and leverage the orthogonal matching pursuit (OMP) algorithm to obtain the channel estimator. Finally, simulation results are provided to corroborate our proposed studies.

Recent results in shallow water acoustic channel estimation and equalization

Shallow water acoustic channel estimation and equalization has been widely studied and explored across the last three decades, with several techniques being proposed that account for the rapidly time-varying nature of the channel. Despite advances in integrating robust channel equalization techniques with channel estimation, incorporating well-known models of acoustic propagation into the equalization setup has been hard. In this work, we will present some of our recent explorations in this regard involving a hybrid setting as well as a model-based simulation to test the performance of simple equalization schemes under a variety of channel conditions. The shallow water acoustic channel is first estimated from experimental field data, and the real-time channel estimates are used to simulate an end-to-end communication system with ambient noise at different signal-to-noise ratios (SNR). In tandem, we also simulate a time-varying shallow water acoustic channel under similar oceanic conditions using the Bellhop model, along with a communication system with similar signaling schemes and SNR variations. Equalization results from both the hybrid and model-based settings will be presented and implications of including machine learning to learn the channel parameters from both setups to inform the channel equalizers will be explored.

Estimation and interpretation of multipath channel activity using braiding techniques

Robust underwater acoustic channel estimation is critical towards improving communications efforts and enhancing awareness of changing environments. To explore these channels in-depth, machine learning algorithms are developed through feature geometric representations, referred to as “braiding,” to interpret multipath ray bundles within shallow water acoustic channels in two ways. The first application of this work predicts the number of reflections an acoustic signal may undergo through the environment by applying known physical parameters and braid features. The second application explores the importance of a braid feature within the acoustic channel for estimation purposes by using braid path information. Three unique machine learning techniques are trained to predict the applications using a diverse set of shallow water acoustic channels generated through the BELLHOP model. Machine learning models developed for the applications demonstrate high testing accuracies with an accuracy of 86.70% in the first application and an accuracy of 99.94% in the second application. As a further demonstration, braid feature representations and model predictions are used for channel estimation and determining the number of reflections using SPACE08 field data.

Smart green ocean underwater IoT network by ICA-based acoustic blind MIMO OFDM transceiver

Multi-Input Multi-Output (MIMO) telecommunication systems with Orthogonal Frequency Division Modulation (OFDM) scheme support high rate data exchange which can be efficiently applied to the fast-growing networks of Internet of Things (IoT). In the case of Underwater IoT (UIoT) where the strength of electromagnetic waves rapidly falls off, the MIMO network can be effectively implemented by substitute of acoustic OFDM instead of the normal one. Due to these advantages, this research work suggests ICA based acoustic MIMO OFDM for UIoT wherein the data rate is further improved as the MIMO OFDM is implemented based on blind channel estimation and multi-user identification without the need for using any pilot and preamble data. Also, theoretical analysis shows the data rate increases and energy consumption decreases due to ICA-based MIMO acoustic channel estimation. In the case of the under-work UIoT network, it achieves 52.38% higher data rate, and 41.67% less energy consumption. As another specific result, the proposed method shows high efficiency on sparse underwater channels and as the channel sparseness gets lower, the efficiency decreases.

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.

MyoMonitor: Evaluating muscle fatigue with commodity smartphones.

Muscle fatigue is common among humans and also a crucial indicator of many muscular diseases such as muscular dystrophy and disorders. Timely evaluation of muscle fatigue, hence, is important to track disease progress and avoid disease exacerbations. However, convenient tools for evaluating muscle fatigue out of clinic are still missing. In this paper, we present a new technique that uses commodity smartphones to evaluate muscle fatigue through simple and daily muscle exercises. The basic idea of our technique is to mimic an active sonar system with the smartphone's built-in microphone and speaker, and use this sonar system to evaluate muscle fatigue from the muscle's surface characteristics that can be measured from the transmitted acoustic signal. More specifically, our technique first measures the acoustic channel disturbances caused by fatigue-induced muscle tremor via channel estimation, and then derives quantitative fatigue levels from the variation of acoustic channel estimation. By using the arm bicep muscle as our primary target, we designed the exercise protocol and implemented a smartphone app for fatigue evaluation. Experiment results verified that our technique can precisely evaluate the speed of muscle fatigue accumulation, as well as identifying the actual fatigue occurrence. This technique, hence, could be used in practical home settings for effective fatigue evaluation on a daily basis.

Virtual Time-Inverse OFDM Underwater Acoustic Channel Estimation Algorithm Based on Compressed Sensing

The severe multipath delay of the underwater acoustic channel, the Doppler shift, the severe time-varying characteristics, and sparsity make it difficult to obtain the channel state information in the channel estimation of the virtual time-reverse mirror OFDM, which makes the virtual time mirror subcarrier orthogonality easy to suffer damage; the focusing effect is not obvious. Therefore, this paper proposes a virtual time-inverse OFDM underwater acoustic channel estimation algorithm based on compressed sensing. The algorithm extracts the detection signal, constructs a sparse signal model of the delay-Doppler shift, and then performs preestimation of the underwater acoustic channel based on the compressed sensing theory. Then, by predicting the timing of the underwater acoustic channel and convolving with the received signal, the algorithm improves the focusing effect better. Experimental simulations show that compared with LS and OMP algorithms, the algorithm can accurately recover channel information from a small number of observations, reduce the bit error rate by 10%, and improve the accuracy of channel estimation and the time-inverse OFDM performance of virtual time.

L1 norm-recursive least squares algorithm for the robust sparse acoustic communication channel estimation

L1 norm-recursive least squares algorithm for the robust sparse acoustic communication channel estimation

Exploiting Spatial–Temporal Joint Sparsity for Underwater Acoustic Multiple-Input–Multiple-Output Communications

Multiple-input–multiple-output (MIMO) system offers a promising way for high data rate communication over bandwidth-limited underwater acoustic channels. However, MIMO communication not only suffers from intersymbol interference, but also introduces the additional co-channel interference, which brings challenge for underwater acoustic MIMO channel estimation and for channel equalization. In this article, we propose novel interference cancellation (IC) methods for handling this co-channel interference problem in the design of both channel estimation and channel equalization. Our method for channel estimation utilizes the spatial joint sparsity and the temporal joint sparsity in the multipath structure to estimate sparse channels with common delays under distributed compressed sensing framework. In this way, we enhance channel estimates with common delays, thus, suppress co-channel interference. Meanwhile, to address the case of multipath arrivals with different delays, which are estimated as noise under simultaneous orthogonal matching pursuit (SOMP) algorithm, we introduce forward–reverse strategy to SOMP algorithm, which is referred to as the FRSOMP algorithm. Our proposed FRSOMP algorithm performs the SOMP algorithm to achieve the initial channel estimates, performs the forward-add process, which attempts to add promising candidates into support sets, and performs the reverse-fetch process to check if the candidates in the support set are retained or removed. The purpose of channel estimation is to directly calculate the filter coefficients for channel-estimation-based decision feedback equalization (CE-DFE). In this article, we also propose a novel CE-DFE receiver with IC component. We design IC filters based on the traditional CE-DFE, and we derive the coefficients of the feedforward filters, feedback filters, and IC filters based on the channel estimate metric obtained by the FRSOMP algorithm, so the co-channel interference will be suppressed both in channel estimation and channel equalization. We demonstrate the performance of our approach by numerical simulation, lake experiment, and sea experiment. Results are provided to demonstrate the effectiveness of the proposed methods, which show that the proposed methods obtain higher output signal-to-noise ratio, lower bit error rate, and more separated constellations compared with the traditional compressed sensing channel estimation method and the traditional CE-DFE method.

Underwater Acoustic Sparse Channel Estimation Based on DW-SACoSaMP Reconstruction Algorithm

The compressive sampling matching pursuit (CoSaMP) algorithm is widely used in orthogonal frequency division multiplexing (OFDM) underwater acoustic (UWA) sparse channel estimation with good estimation accuracy. However, the algorithm requires channel sparsity a priori information as well as a large number of columns in the measurement matrix because of the large delay of the UWA channel during channel estimation, which greatly increases the complexity of CoSaMP. Therefore, a sparsity adaptive CoSaMP based on a dynamic threshold and weak selection of atoms (DW-SACoSaMP) is proposed in this letter. The algorithm can quickly estimate the channel impulse response (CIR) without channel sparsity when used for UWA sparse channel estimation. Simulation results show that, compared with other reconstruction algorithms, the estimation accuracy of this algorithm is higher and the complexity is lower; when the signal to noise ratio (SNR) is greater than 8dB, the advantage of estimation accuracy is more obvious.

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.

An time-varying acoustic channel estimation using least squares algorithm with an average gradient vector based a self-adjusted step size and variable forgetting factor

An time-varying acoustic channel estimation using least squares algorithm with an average gradient vector based a self-adjusted step size and variable forgetting factor

Underwater Acoustic OFDM Channel Estimation with Unknown Sparsity

Underwater acoustic channels estimation accuracy seriously affects the demodulation performance of the receiver in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communications. Many channel estimation algorithms based on compressed sensing (CS) have been proposed. However, these algorithms usually needs the information of sparsity, while the information of sparsity is unavailable in many UWA OFDM communications. In this paper, a novel channel estimation algorithm based on fractional Fourier transform (FRFT) and orthogonal matching pursuit (OMP) is proposed, FRFT is introduced to process the synchronization signal, so sparsity is estimated. OMP is adopted to reconstruct channel impulse response (CIR). The most innovative feature of the proposed algorithm is the estimation of sparsity, which makes OMP algorithm more practical. Simulation results show that the proposed algorithm outperforms the least square (LS) and in UWA OFDM communication.