Underwater Acoustic Channel Research Articles

SPACNet: A Simulation Platform of an Acoustic Cognitive Network

Originating from the concept of cognitive networks (CNs), which are becoming popular in wireless terrestrial communication scenarios, underwater acoustic cognitive networks (UACNs) are drawing more and more attention in the field of the Internet of Underwater Things (IoUT). However, as the implementation of cognitive mechanisms in underwater acoustic networks is different from that of wireless scenarios, it is impossible or difficult for traditional simulation platforms to carry out simulations of UACNs. There is a lack of specialized simulation tools in terms of UACNs. To enable the quantitative evaluation of the effectiveness and performance enhancement of a UACNs in an adverse underwater environment, a simulation platform of acoustic cognitive networks (SPACNet) was designed and investigated in this article. First, based on a state machine-based protocol programming framework, the SPACNet is capable of supporting the implementation of different state-transform types associated with cognitive networking protocols. Moreover, to facilitate the realization of cognitive function at comprehensive levels of signal, information, and link, an underwater acoustic channel model with an environmental parameter input is integrated in SPACNet to generate underwater environment-driven multiple-aspect behaviors. Moreover, a simplified collision model consisting of an environment factor, channel response, and node location is used to reduce the complexity of the simulation of UACNs signal reception. A simulation was carried out to verify the effectiveness of SPACNet in evaluating the cognitive capabilities of UACNs. Finally, a field UACNs experiment was performed to validate the general consistency between the conclusion obtained with the SPACNet-based simulation and that from the field test.

Short-Block-Length Low-Density Parity-Check Codes-Based Underwater Acoustic Spread-Spectrum Communication System

Low-density parity-check (LDPC) codes are commonly used in communication systems to improve the system performance, but LDPC codes takes too long for decoding, making communication inefficient and unsuitable for short-frame data transmission methods. In underwater acoustic channels, spread-spectrum communication becomes an effective way to realize long-distance communication. This paper combines short-block LDPC codes with a direct sequence spread spectrum and soft spread spectrum in underwater acoustic communication, addressing the problem of the inapplicability of conventional LDPC codes. The applicability of the proposed method in this paper is verified through simulation tests and pool experiments. The results indicate that the proposed communication system achieves lower bit error rates compared to the classical coding methods used in underwater acoustic spread-spectrum communication systems under the same channel conditions.

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction.

The Underwater Acoustic Sensor Network (UASN) is a large network in which the vicinity of a transmitting node is made up of numerous operational sensor nodes. The communication process may be substantially disrupted due to the underwater acoustic channel's time-varying and space-varying features. As a result, the underwater acoustic communication system faces the problems of reducing interference and enhancing communication effectiveness and quality through adaptive modulation. To overcome this issue, this paper intends to propose a model for optimal path selection and secured data transmission in UASN via Long Short-Term Memory (LSTM) based energy prediction. The proposed model of transmitting the secured data in UASN through the optimal path involves two major phases. Initially, the nodes are selected under the consideration of constraints like energy, distance and link quality in terms of throughput. Moreover, the energy is predicted with the aid of LSTM and the optimal path is selected with the proposed hybrid optimization algorithm termed as Pelican Updated Chimp Optimization Algorithm (PUCOA), which is the combination of two algorithms including the Pelican Optimization Algorithm (POA) and Chimp Optimization Algorithm (COA). Further, the data is transmitted via the optimal path securely by encrypting the data with the proposed improved blowfish algorithm (IBFA). At last, the developed LSTM+PUCOA model is validated with standard benchmark models and it proves that the performance of the proposed LSTM+PUCOA model attains 90.85% of accuracy, 92.78% of precision, 91.78% of specificity, 89.79% of sensitivity, 7.21% of FPR, 89.76% of F1 score, 89.77% of MCC, 10.20% of FNR, 92.45% of NPV, and 10.22% of FDR for Learning percentage 70.

BIT ERROR PERFORMANCE ENHANCEMENT FOR UNDERWATER ACOUSTIC NOISE CHANNEL BY USING CHANNEL CODING

The harm that underwater noise pollution poses to aquatic ecosystems and the resources that support it are being acknowledged on a worldwide scale. Fisheries and ecotourism are only two of the important businesses that are impacted by noise pollution. Reducing underwater impact noise is a major challenge for underwater acoustic communication systems. However, the implementation of noise reduction measures (noise abatement) remains limited. Most communication systems assume that the noise is both additive and Gaussian. Underwater Acoustic Noise (UWAN) systems generally perform poorly because of the often large non-Gaussian components in intermittent noise in the ocean. This study presents an experimental model (Dolphin-EAR DE200 Series) sound channel noise underwater at Lake Diyala Hamrin, Iraq, using a hydrophone model. Low data volume, multipath propagation, low bandwidth, and higher bit error rate (BER) of received data are major issues for underwater communication systems. In this paper, the Underwater Acoustic Channel (UWAC) aspect is evaluated and an error performance term is determined from the noise in the Student's t-distribution. In addition, Signals using binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) are used to generate error power analysis.

Cluster-aware channel estimation with deep learning method in deep-water acoustic communications.

In underwater acoustic (UWA) communications, channels often exhibit a clustered-sparse structure, wherein most of the channel impulse responses are near zero, and only a small number of nonzero taps assemble to form clusters. Several algorithms have used the time-domain sparse characteristic of UWA channels to reduce the complexity of channel estimation and improve the accuracy. Employing the clustered structure to enhance channel estimation performance provides another promising research direction. In this work, a deep learning-based channel estimation method for UWA orthogonal frequency division multiplexing (OFDM) systems is proposed that leverages the clustered structure information. First, a cluster detection model based on convolutional neural networks is introduced to detect the cluster of UWA channels. This method outperforms the traditional Page test algorithm with better accuracy and robustness, particularly in low signal-to-noise ratio conditions. Based on the cluster detection model, a cluster-aware distributed compressed sensing channel estimation method is proposed, which reduces the noise-induced errors by exploiting the joint sparsity between adjacent OFDM symbols and limiting the search space of channel delay spread. Numerical simulation and sea trial results are provided to illustrate the superior performance of the proposed approach in comparison with existing sparse UWA channel estimation methods.

Cosine function variable step-size transform domain least mean square algorithm based on matrix rotation

A novel cosine function variable step-size transform domain least mean square (LMS) algorithm based on matrix rotation (COS-ROTDCT-LMS-DFE) was put forward. Based on the discrete cosine transform domain LMS (DCT-LMS) algorithm, it was first processed by matrix rotation in an anticlockwise direction to resolve the problem of self-interference between signals, which effectively sped up the convergence of the algorithm. Subsequently, a cosine function variable step-size algorithm was developed considering that its curve was characterized by a smooth top and fast rising and falling speed so as to further improve the convergence speed of the algorithm. Subsequently, the decision feedback equalizer (DFE) was introduced into the algorithm to compensate signal distortion and noise. The convergence performance of the algorithm was simulated in typical multipath underwater acoustic channels. The simulation results showed that the convergence performance of the new algorithm was equivalent to that of recursive least squares (RLS). The convergence speed of COS-ROTDCT-LMS-DFE was even better than that of the recursive least square decision feedback equalizer (RLS-DFE) algorithm. The new algorithm performed well in terms of both convergence speed and steady-state error and could be easily implemented.

Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism

Deep Learning Prediction of Time-Varying Underwater Acoustic Channel Based on LSTM with Attention Mechanism

Cooperative Formation Control for Multiple AUVs With Intermittent Underwater Acoustic Communication in IoUT

Autonomous Underwater Vehicle (AUV) system has played an important role in complex Internet of Underwater Things (IoUT). As we know, single AUV is inefficient in collecting data in large-scale IoUT. In order to improve the timeliness of data, this paper uses multiple AUVs with specific formation shape to collect sensory data. However, the intermittent and unreliable characteristics of underwater acoustic communication between AUVs seriously affects the performance of formation control. To solve this issue, this paper mainly investigates formation control problem of leader-follower structured AUVs with state prediction estimation under the condition of unreliable underwater acoustic channel. Firstly, a novel formation control law derived from backstepping sliding mode method is proposed, and then a suitable Lyapunov functional is constructed to prove sufficient stability conditions. Secondly, Gaussian prediction model considering time delay and packet dropout has been designed to estimate the intermittent communication channel. Moreover, a new metric named formation uniform degree (FUD) is proposed to measure the degree of queue uniformity in a quantitative manner. Finally, extensive simulation results compared with traditional methods based on ideal channel model demonstrate the effectiveness of proposed formation control method.

A Distributed Intelligent Buoy System for Tracking Underwater Vehicles

Underwater vehicles play a crucial role in various underwater applications, such as data collection in underwater sensor networks, target detection and tracking, and underwater pipeline monitoring. Real-time acquisition of their states, particularly their location and velocity, is vital for their operation and navigation. Consequently, the development of a remote tracking system to monitor these states is essential. In this paper, we propose a system that can track the underwater vehicle’s location and velocity. We take a systematic approach that encompasses the system architecture, system composition, signal processing, and mobility state estimation. We present the system architecture and define its components, along with their relationships and interfaces. The beacon signal employed in the system features dual-hyperbolic-frequency-modulated (HFM) waveform and an OFDM symbol with cyclic prefix (CP). Based on this beacon signal, we demonstrate how signal processing techniques are utilized to precisely determine the time of arrival and reduce false alarm rates in underwater acoustic channels affected by impulsive noise. Additionally, we explain how the CP-OFDM symbol is used to measure the Doppler scaling factor and transmit essential information for localization and velocity estimation purposes. Utilizing the measurements obtained through signal processing, least squares estimators are used for estimating both the location and velocity. To validate the effectiveness of our approach, we implement the system and conduct field trials. Two separate experiments were conducted in which the diagonal lengths of the square topology were designed to be 1000 m and 800 m. The minimum/maximum root mean square error of localization in the first and second experiment is 2.36/2.91 m and 1.47/2.49 m, respectively. And the minimum/maximum root mean square error of velocity estimation in the first and second experiment is 0.16/0.47 m/s and 0.21/0.76 m/s, respectively. Results confirm the effectiveness of the proposed method in estimating the location and velocity of the underwater vehicle. Overall, this paper provides a practical and effective design of a system to track the location and velocity of underwater vehicles. By leveraging the proposed system, signal processing, and mobility state estimation methods, our work offers a systematic solution. And, the successful field experiment serves as evidence of the feasibility and effectiveness of the proposed system, making it a valuable contribution to the field of tracking underwater vehicles.

A Modulation Recognition System for Underwater Acoustic Communication Signals Based on Higher-Order Cumulants and Deep Learning

Underwater acoustic channels, influenced by time-varying, space-varying, frequency-varying, and multipath effects, pose significant interference challenges to underwater acoustic communication (UWAC) signals, especially in non-cooperative scenarios. The task of modulating and identifying distorted signals faces huge challenges. Although traditional modulation recognition methods can be useful in the radio field, they often prove inadequate in underwater environments. This paper introduces a modulation recognition system for recognizing UWAC signals based on higher-order cumulants and deep learning. The system achieves blind recognition of received UWAC signals even under non-cooperative conditions. Higher-order cumulants are employed due to their excellent noise resistance, enabling the differentiation of OFDM signals from PSK and FSK signals. Additionally, the high-order spectra differences among signals are utilized for the intra-class recognition of PSK and FSK signals. Both simulation and lake test results substantiate the effectiveness of the proposed method.

Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation

Accurate channel estimation is the fundamental requirement for recovering underwater acoustic orthogonal time–frequency space (OTFS) modulation signals. As the Doppler effect in the underwater acoustic channel is much more severe than that in the radio channel, the channel information usually cannot strictly meet the compressed sensing sparsity assumption in the orthogonal matching pursuit channel estimation algorithm. This deviation ultimately leads to a degradation in system performance. This paper proposes a novel approach for OTFS channel estimation in underwater acoustic communications, utilizing a model-driven deep learning technique. Our method incorporates a residual neural network into the OTFS channel estimation process. Specifically, the orthogonal matching pursuit algorithm and denoising convolutional neural network (DnCNN) collaborate to perform channel estimation. The cascaded DnCNN denoises the preliminary channel estimation results generated by the orthogonal matching pursuit algorithm for more accurate OTFS channel estimation results. The use of a lightweight DnCNN network with a single residual block reduces computational complexity while still preserving the accuracy of the neural network. Through extensive evaluations conducted on simulated and experimental underwater acoustic channels, the outcomes demonstrate that our proposed method outperforms traditional threshold-based and orthogonal matching pursuit channel estimation techniques, achieves superior accuracy in channel estimation, and significantly reduces the system’s bit error rate.

An iterative subblock-based receiver for SC-FDE systems in time-varying underwater acoustic channels

Traditional single-carrier frequency domain equalization (SC-FDE) systems assume time-invariant channels that are difficult to realize in underwater acoustic (UWA) communication over block-based durations, leading to inter-frequency interference (IFI). Currently available approaches to resolving this issue involve shortening the block or inserting frequent training sequences at the cost of additional information from the channel and reduced spectral efficiency. This paper proposes an iterative subblock-based receiver algorithm that divides each time-varying block into multiple quasi-static subblocks. The designed receiver has a two-step structure consisting of block processing and cascaded subblock processing, where the former generates prior symbolic knowledge and the latter iteratively weakens IFI. We consider smoothly variant channels between adjacent subblocks and track them using a Kalman filter, leading to a more accurate estimation of the IFI and better equalization. The proposed algorithm was tested through simulations and experiments in Qiandao Lake and the Yellow Sea using blocks of different lengths and numbers of subblocks and elements in the array. The results provide a low bit error rate.

Shallow Water Experiment of OFDM Underwater Acoustic Communications

The large variability of communication properties of underwater acoustic channels, and especially the strongly varying instantaneous conditions in shallow waters, is a challenge for the designers of underwater acoustic communication (UAC) systems. The use of phase modulated signals does not allow reliable data transmission through such a tough communication channel. However, orthogonal frequency-division multiplexing (OFDM), being a multi-carrier amplitude and phase modulation technique applied successfully in the latest standards of wireless communications, gives the chance of reliable communication with an acceptable error rate. This paper describes communication tests conducted with the use of a laboratory model of an OFDM data transmission system in a shallow water environment in Wdzydze Lake.

Methods to Estimate the Channel Delay Profile and Doppler Spectrum of Shallow Underwater Acoustic Channels

In this paper, we present the methods to detect the channel delay profile and the Doppler spectrum of shallow underwater acoustic channels (SUAC). In our channel sounding methods, a short impulse in form of a sinusoid function is successively sent out from the transmitter to estimated the channel impulse response (CIR). A bandpass filter is applied to eliminate the interference from out-of-band (OOB). A threshould is utilized to obtain the maximum time delay of the CIR. Multipath components of the SUAC are specified by correlating the received signals with the transmitted sounding pulse with its shifted phases from 0 to 2Π. We show the measured channel parameters, which have been carried out in some lakes in Hanoi. The measured results illustrate that the channel is frequency selective for a narrow band transmission. The Doppler spectrum can be obtained by taking the Fourier transform of the time correlation of the measured channel transfer function. We have shown that, the theoretical maximum Doppler frequency fits well to that one obtained from measurement results.

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication.

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

A deep-learning based high-gain method for underwater acoustic signal detection in intensity fluctuation environments

To achieve efficient underwater acoustic (UWA) signal detection in low signal-to-noise ratio (SNR) scenarios, the transmitted signals are designed with a large time-bandwidth product to get a high detection gain. The linear correlator (LC) is considered as the maximum SNR detector, whose detection gain is proportional to the time-bandwidth product. However, the detection performance of LC degrades significantly in time-variant multipath UWA channel and non-Gaussian UWA ambient noise. In this study, we present a deep-learning based two-stage UWA signal detection method in intensity fluctuation environments. This method takes the advantages of the Conv-TasNet and Encoder-Decoder network, which utilizes an encoder module to extract signal features, a separation module to enhance the signal components and then another decoder module to reconstruct the transmitted signal. To demonstrate the performance of the proposed method, the datasets used for training and testing originated from the ASIAEX 2001 South China Sea (SCS) experiment. The experimental results show that our model outperforms the classical LC and channel estimation based LC (CE-LC) in constant false alarm rate (CFAR) detection and also surpasses the TCDAE and Conv-Tasnet models as evaluated by DSI-SNR1 and DSI-SNR2.

Underwater Acoustic Channel Estimation via an Orthogonal Matching Pursuit Algorithm Based on the Modified Phase-Transform-Weighted Function

In the context of torpedo guidance systems, the performance of active sonar in channel parameter estimation and target detection and recognition is significantly degraded by the multipath effect and the time-varying characteristics of the underwater acoustic (UWA) channel. Therefore, it is urgent to propose an algorithm that can accurately estimate the channel parameters in multipath time-varying UWA channels. To solve these problems, this study developed a modified phase transform (PHAT)-weighted function and applied it to the orthogonal matching pursuit (OMP) algorithm, named M-PHAT-OMP. The proposed algorithm is more robust, improves the resolution of the time delay and further improves the estimation accuracy of the parameters in the case of motion. Furthermore, with the aim of solving the problem of the difficulty that the traditional OMP algorithm has in determining sparsity, this study proposes a joint-threshold method, where the threshold value serves as the condition for terminating the algorithm’s iteration. The simulation results demonstrate that the M-PHAT-OMP algorithm proposed in this study exhibits a superior performance compared to other algorithms, as evidenced by its lower root mean square error (RMSE) for delay. Moreover, the experimental results also validate that the proposed algorithm has superior robustness and resolution of the time delay in practical applications.

Channel Estimation for Underwater Acoustic OFDM Communications: Recent Advances

Background: To resist the time-variant underwater acoustic (UWA) channel, large amounts of channel estimation algorithms for the UWA orthogonal frequency division multiplexing (OFDM) are presented. An updated review of the recent UWA OFDM channel estimators is suggested in this article. Objective: The goal of this work is to review and conclude the development of different types of channel estimators. The possible perspectives about the future UWA channel estimator design are also discussed. Methodology: The principles and performances of the linear channel estimators, the compressed sensing (CS)-based channel estimators, and the neural network (NN)-based channel estimators are reviewed and discussed. Simulations are conducted to compare the typical implementations of the different methods. Conclusion: To take more channel state characteristics into account, the data-driven methods have been applied in the channel estimator design. Compared with the linear and CS-based methods, the NN-based channel estimator shows the higher performance, robustness and lower complexity, which is promising to be applied with the proper structure and training sets.

An In-Phase/Quadrature Index Modulation-Aided Spread Spectrum Communication System for Underwater Acoustic Communication

In a multiple-sequences-spread-spectrum (MSSS) communication system, the transmitted signal is made up of superimposed cyclic shift sequences, each sequence carrying a symbol, so the data rate is inceased. In this context, we propose an in-phase/quadrature index modulation-aided MSSS system (MSSS-I/Q-IM), which achieves higher data rate and better bit-error-rate (BER) performance. The new system maps the additional bits to the cyclic shift length of the superimposed sequences of I/Q branches. The sequences in two branches carry the real and imaginary parts of the M-ary phase-shift-keying (MPSK) symbols, respectively. We derive the theoretical BER of the MSSS-I/Q-IM system over an additive white Gaussian noise (AWGN) channel. Then, the simulation experiments of the proposed system and MSSS system over an underwater acoustic channel are analyzed and compared. Finally, field experiments are carried out in the pool environment. The experiment results show that the proposed scheme can achieve higher data rate or better BER performance via adjusting the IM parameter. This is important for the time-variant underwater acoustic transmission environments.

An Efficient and Robust Underwater Image Compression Scheme Based on Autoencoder

Images occupy a prominent place in data because they are more visually appealing than sounds or texts. Acoustic waves are the only feasible alternative for long-distance underwater transmission since seawater has a high absorption impact on lighting and electromagnetic signals. However, underwater acoustic (UWA) communication technology can only provide relatively limited bandwidth (low effectiveness) and insufficiently stable links (low reliability). As a result, it is challenging to send high-resolution underwater images via the UWA channel. This article proposes an effective and robust underwater image compression scheme. First, an autoencoder is used for underwater image extreme bit rate compression. Then, a multistep training strategy is proposed to improve the robustness of the decoder by gradually learning channel degradation features. Finally, the autoencoder encodes images in two paths to achieve efficient compression and higher image quality when reconstructing images. The main path completes the compression task with a low bit rate and high robustness, while the branching path implements the image block retransmission compensation through the feedback signal. The experimental results demonstrate that the content of the reconstructed image can still be recognized under the conditions of a compression ratio of up to 1/768 and an average bit error rate of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{10}^{-1}$</tex-math></inline-formula> . The joint multistep training strategy and multidescription coding achieve a low bit rate and high robustness for underwater image communication, which has good application prospects.