Underwater Acoustic Transmission Research Articles

Underwater acoustic non-reciprocal manipulation based on dynamic-modulation structures

Abstract Underwater acoustic non-reciprocal transmission via dynamic-modulation structures with time-varying mass and stiffness is studied. The model system consists of spatiotemporally modulated discrete lattices immersed in the water background. Based on the transfer matrix method, an analytic model for the coupled continuum-discrete system is developed to calculate acoustic scattering responses in the frequency domain. Finite-difference time-domain computation is conducted for the coupled system to verify the theoretical model. Results show that acoustic non-reciprocal transmission in opposite directions appears at frequencies where there are asymmetric bandgaps in dispersion diagrams. Asymmetric transmission can be enhanced in magnitude by engineering the modulating amplitudes of time-varying parameters or increasing the number of lattice elements, while the frequency bandwidth can be broadened by cascading structural elements with different modulating frequencies due to the gap-combining effect. The model may find potential applications in underwater acoustic isolation and sonar communication.

Predicting underwater acoustic transmission loss in the SOFAR channel from ray trajectories via deep learning.

Predicting acoustic transmission loss in the SOFAR channel faces challenges, such as excessively complex algorithms and computationally intensive calculations in classical methods. To address these challenges, a deep learning-based underwater acoustic transmission loss prediction method is proposed. By properly training a U-net-type convolutional neural network, the method can provide an accurate mapping between ray trajectories and the transmission loss over the problem domain. Verifications are performed in a SOFAR channel with Munk's sound speed profile. The results suggest that the method has potential to be used as a fast predicting model without sacrificing accuracy.

Cyclostationary Feature Distortion for Secure Underwater Acoustic Transmissions

Cyclostationary Feature Distortion for Secure Underwater Acoustic Transmissions

Research on developing an adaptive algorithm for enhancing the quality of underwater positioning based on the sonar USBL principle

The paper proposes an enhanced solution for improving the accuracy of underwater object positioning based on the Ultra-Short Baseline (USBL) sonar principle. In shallow water regions, complex hydrological phenomena affect the quality of underwater acoustic signal transmission and reception, making it essential to enhance the processing of these signal groups. Through the USBL system model, the paper presents a model capable of adaptively selecting accurate signals amidst the noisy underwater environment. The proposed model is implemented and validated in practice using FPGA hardware in Lan Ha Bay. The results demonstrate the effectiveness of the proposed algorithm compared to conventional approaches, as it extends the management range and optimizes the transmission power of the system, even in cases of low signal-to-noise ratio.

Incentive Oriented Two-Tier Task Offloading Scheme in Marine Edge Computing Networks: A Hybrid Stackelberg-Auction Game Approach

With the increasing exploration of marine resources, various marine wireless devices have been rapidly deployed for different marine applications such as marine navigation, ocean environment monitoring, and seabed resource exploitation. However, due to long transmission delay and low data rate between marine wireless devices and the cloud, it is challenging to satisfy the service requirements of computing-intensive and delay-sensitive tasks. By migrating computing resources from cloud to the near side of ocean, the paradigm of marine edge computing networks, which integrates communication and computation capacities in marine wireless devices, is expected to support a variety of marine tasks (e.g., data collection, monitoring and processing) with low delay and high data rate. However, considering the rationality and selfishness of marine wireless devices and their limited computing-capacity, how to motivate marine wireless devices to conduct task processing becomes an important problem for improving computing efficiency. To address this issue, in this paper, we propose an incentive oriented two-tier task offloading scheme for marine edge computing networks via hybrid Stackelberg-auction game approach, with the objective of improving the offloading efficiency and maximizing marine wireless devices’ utilities. Specifically, for underwater acoustic transmission tier, we exploit multi-access task offloading scheme, in which underwater wireless sensor (UWS) uploads its workloads to an unmanned underwater vehicle (UUV) and a sea surface sink node (SN) via non-orthogonal multiple access (NOMA) transmission. We formulate the utility of each party and model the task offloading process among UWS, UUV and SN as a Stackelberg game to optimize the UWS’s offloading strategy, UUV’s and SN’s price strategies. For radio frequency transmission tier, SN can offload its partial workloads to an unmanned aerial vehicle (UAV) via frequency division multiple access (FDMA) transmission. We provide their utilities and model the offloading process between a SN and a UAV as a double auction game to optimize their bidding strategies. Extensive simulation results are provided to validate the performance of the proposed scheme. Numerical results demonstrate that the proposed algorithms can obtain the optimal solutions and increase the utilities for marine wireless devices.

Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application

In recent years, underwater acoustic applications have attracted much attention, for example, for underwater environmental monitoring, underwater exploration, etc. Hydrophones play a particularly important role. Although hydrophone design has been in multifarious application forms, it still needs to consider increasing demand for low-cost, low-consumption, and multiple-function devices, as well as issues around miniaturization, lossless data collection, etc. In this paper, we design a compact underwater acoustic device that has the capability of underwater acoustic signal storage, underwater acoustic signal transmission via the Internet, and decoding based on the direct sequences spread spectrum (DSSS). The key problem is how to implement multiple functions in only one micro-controller unit (MCU). The hardware and software of the proposed multi-function hydrophone are described in detail. In particular, the MCU, the pre-amplifier with gain control, and the analog-to-digital integrated chip are introduced. Moreover, underwater acoustic data storage, underwater acoustic transmission, and the DSSS receiver are depicted in terms of software. The different functions of the hydrophone are verified in sea trial experiments. The results show that the proposed multi-function hydrophone is able to sample underwater acoustic data at high quality. In addition, to demonstrate configurable parameters, the DSSS receiver with different carrier frequencies is provided. The proposed multi-function hydrophone realizes zero bit error rate (BER) when carrier frequency fc=9 kHz, and the BER with 10−3 order of magnitude when carrier frequency fc=15.5 kHz. The results show that the proposed multi-function hydrophone has great potential to explore the ocean.

Virtual Space-Time DiversityTurbo Equalization Using Cluster Sparse Proportional Recursive Least Squares Algorithm for Underwater Acoustic Communications

The oceanic positioning, navigation and timing (PNT) network requires high-quality underwater acoustic message transmission. Turbo equalization technology has exhibited superior performance for underwater acoustic (UWA) communications compared with conventional channel equalizers. To overcome the performance reduction caused by severe doubly selective UWA channels, the virtual space-time diversity soft direct-adaptation turbo equalization is proposed for UWA communications. The proposed scheme improves the ability of the typical turbo equalizer to deal with both Doppler and multipath effects for time varying channels. We utilize a fractionally spaced soft interference cancellation equalizer (FS-SE) instead of a hard decision to constitute the soft-input soft-output (SISO) equalizer. Combined with another virtual time-reversal mirror equalizer component, we can obtain virtual space and time diversity with only a single receiving transducer and mitigate the error propagation phenomenon of the feedback filter. To satisfy the sparse UWA channel, the ℓp,q-PRLS algorithm is applied to adaptive updates for FS-SE. In the proposed scheme, an adjustable interpolator and digital phase-locked loop are embedded into the equalizer to overcome the residual Doppler frequency shift and recover the timing distortion. Results of simulations and field lake trial show that the proposed scheme achieves better performance than existing ones under the same equalizer order.

A Constellation Diagram Learning-Based Adaptive Sparse Nonorthogonal Wavelet Division Multiplexing for Sonar Image Underwater Acoustic Transmission

In underwater Internet of Things (IoT) applications, efficient and reliable transmission is demanding. Recently, the non-orthogonal multicarrier (MC) modulation is utilized as a promising spectrum-efficient solution in the UWA channel, where generally, the constellation mapping (CM) methods (such as 8-PSK and 16-QAM) are utilized to further increase the transmission rate. In this paper, we focus on the transmission of sonar images among IoT nodes and a constellation diagram learning-based adaptive sparse non-orthogonal wavelet division multiplexing (CDLA-SNOWDM) system is proposed to combat complex channel states between IoT nodes. We found that different CM methods contribute to unique time-frequency characteristics in constellation diagrams. We first utilize the time-frequency characteristics in designing the non-orthogonal subcarriers. The complex-valued CM signals are regarded as a new type of image. We introduce dictionary learning (DL) to non-orthogonal subcarrier designing, with the idea that designing CM signal adaptive non-orthogonal subcarriers can be regarded as designing an adaptive subcarrier dictionary. The CDLA-SNOWDM modulation is performed as a projection of the CM image to the subcarriers with an adaptive dictionary. The learned adaptive subcarriers can represent constellation diagrams more efficiently and improve transmission reliability. Both simulations and experiments show that the proposed scheme improved the reliability in transmitting sonar images over other orthogonal and non-orthogonal modulation schemes in UWA scenarios.

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.

Secure Underwater Distributed Antenna Systems: A Multi-Agent Reinforcement Learning Approach

Dear Editor, Underwater distributed antenna systems (DAS) are stationary infrastructures consisting of multiple geographically distributed antenna elements (DAEs) which are interconnected through high-rate backbone networks [1]. Compared to centralized systems, the DAS could provide a larger coverage area and higher throughput for underwater acoustic (UWA) transmissions. In this work, exploiting the low sound speed in water, a multi-agent reinforcement learning (MARL)-based approach is proposed to secure underwater DAS against eavesdropping at the physical layer. Specifically, the theoretical secrecy rate is firstly derived for time-slotted UWA networks (UWANs) considering the large propagation delays. Furthermore, we investigate the long-term sum secrecy rate optimization problem under the MARL framework, where each DAE learns its optimal transmission strategy online. Simulation results show that the proposed method achieves higher secrecy performance compared to competing benchmark methods.

A semantic segmentation-based underwater acoustic image transmission framework for cooperative SLAM

With the development of underwater sonar detection technology, simultaneous localization and mapping (SLAM) approach has attracted much attention in underwater navigation field in recent years. But the weak detection ability of a single vehicle limits the SLAM performance in wide areas. Thereby, cooperative SLAM using multiple vehicles has become an important research direction. The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles. However, the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data. It is essential to compress the images before transmission. Recently, deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks, but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information. In this paper, we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression (SSIC) framework and the joint source-channel codec, to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver. The SSIC framework consists of Auto-Encoder structure-based sonar image compression network, which is measured by a semantic segmentation network's residual. Considering that sonar images have the characteristics of blurred target edges, the semantic segmentation network used a special dilated convolution neural network (DiCNN) to enhance segmentation accuracy by expanding the range of receptive fields. The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data, which deal with sonar image transmission error caused by the serious underwater acoustic channel. Experiment results demonstrate that our method preserves more semantic information, with advantages over existing methods at the same compression ratio. It also improves the error tolerance and packet loss resistance of transmission.

Internet of Underwater Things Infrastructure: A Shared Underwater Acoustic Communication Layer Scheme for Real-world Underwater Acoustic Experiments

The advancement of Underwater acoustic communication (UAC) holds great promise for the Internet of Underwater Things (IoUT). However, a critical challenge to the development of UAC technology is the difficulty of obtaining sea trial data, such difficulty makes the emerging algorithms inconvenient to evaluate. In this paper, we propose a Shared Underwater Acoustic Communication Layer (SUACL) to collect underwater acoustic signal, the proposed SUACL is founded on the IoUT and users are allowed to remotely access the system to arrange underwater acoustic data transmission and reception via Internet. The SUACL system aims to enable the remote operation of communication units that are deployed in designated sea areas. The merit of the proposed SUACL is that the sea trial data can be obtained without risky and high expense, and the emerging underwater acoustic (UWA) technologies can be evaluated in time. The SUACL is composed of the non-underwater part which includes the application layer and fusion layers, and the above/under water part including the communication layer on which we focus. We provide a detailed description of the communication layer of the proposed SUACL system, such as the pre-amplifier with reconfigurable gain, the analog-to-digital converter with reconfigurable sampling rates, the reconfigurable transducers with different frequency bands, the reconfigurable number of hydrophones. The SUACL's reconfigurability options make it a versatile tool that can meet the diverse experimental needs for underwater communication research. In addition, we also describe the mechanisms of how experimental data is exchanged through the SUACL. Our experimental results demonstrate the feasibility and effectiveness of the proposed SUACL in a shallow-water environment.

A non-contacting leak fault diagnosis method for subsea Christmas tree valve based on deep neural network with skip connections

Due to the complex and diverse underwater environment, underwater acoustic transmission is easily disturbed, absorbing many noise components. To deal with the complex noise components is a challenge with the traditional signal processing method. Moreover, it is hard to diagnose the valve's leakage and the leakage degree in the subsea Christmas tree using underwater acoustic signals. This paper proposes a new non-contacting fault diagnosis method based on a deep neural network (DNN) with skip connections. First, we obtain the required time-domain data using acoustic sensors (a digital hydrophone) in experiments. Second, the time-domain data is preprocessed and is converted by the short-time Fourier transform into time-frequency domain signals. The time-frequency domain signals are then input into a constructed DNN model to minimize the noise components in the signal. After that, the denoised data are applied to a convolutional neural network for fault diagnosis. Finally, an underwater acoustic experiment is designed and performed to validate the proposed method's effectiveness. The experimental results demonstrate that our proposed method can effectively diagnose the leakage fault of the valve, and the diagnosis accuracy reaches 98.89%.

An energy-balanced head nodes selection scheme for underwater mobile sensor networks

Underwater sensor networks system is a promising technology for smart ocean monitoring applications. To address the imbalance problem of network overhead and limited energy of underwater sensor nodes, this paper presents a CH–SH Selection and Sink Path-planning (CSSP) scheme to collect vast amount of data from heterogeneous sensor nodes. The scheme firstly establishes a three-layer network structure model for underwater mobile sensor networks (UMSN) based on multi-mode communication mechanism and provides energy-balanced head nodes (cluster head and sub-cluster head nodes) selection algorithm based on particle swarm iterative optimization. Then, the optimal global-path-plan of mobile sink is proposed to visit all the head nodes to collect data, reduce the multi-hop underwater acoustic transmission distance of relay nodes, and avoid the energy hole problem. Lastly, the upper-layer multi-hop network of UMSN is designed to remotely control local-path-plan over mobile sink, in order to implement joint planning of global and local paths of mobile sink. Simulation results verified that the proposed CSSP scheme outperformed 11%, 16% and 22% over three typical protocols in terms of nodes energy consumption, CSSP was 9%, 12% and 19% lower than three typical protocols in terms of SD of energy consumption, packet delivery ratio of CSSP was 8%, 10% and 12% higher than three typical protocols. The scheme could significantly balance energy and reduce packet loss rate.

Time-domain Signal Compressing-Reconstruction Method for Underwater Inclination Data Transmission of a Multi-point Mooring System

Due to the obstacles to underwater acoustic transmission such as narrow bandwidth and frequent data loss, a novel time-domain signal compressing-reconstruction method is proposed for underwater inclination data transmission of a multi-point mooring system. First, a time-frequency analysis was carried out on the measured inclination data and a calculation method using the top tension force of the anchor chain was proposed. Then, to address problems like narrow bandwidth and easy data loss, a time-domain signal compressing-reconstruction algorithm was constructed. The data indicated that the reconstruction error was only 0.6957% with a compression ratio of 20%. Finally, the effects of compression ratios and loss rates on the reconstruction accuracy was analyzed to demonstrate the accuracy, feasibility and reliability of the compression-reconstruction algorithm.

Modelling and Simulation on Acoustic Channel of Underwater Sensor Networks

The reliability of the modelling system mainly depends on the simulation of underwater acoustic channel characteristics. The reliability of simulator is improved because of the use of Bellhop in NS-Miracle. World Ocean Simulation System (WOSS) can retrieve the data of marine environment by accessing the database of seabed depth, sound speed profile and seabed sediment, and transmit them to Bellhop simulator automatically, so that the model is closer to the actual underwater acoustic transmission channel than not using WOSS. In order to verify the reliability of NS2/NS-Miracle simulation system with WOSS, a centralized underwater sensor network with five nodes is simulated on the integrated simulation system. The characteristic empirical model, Bellhop Ray-Tracing model, and WOSS combined with Bellhop model are, respectively, adopted to simulate underwater acoustic channel. The results of three types of simulation, such as average throughput, average delay, and packet error rate, and simulation time are very close under the same condition. It proves that the accuracy of integrated simulation system is as excellent as that of NS-Miracle. However, WOSS can automatically acquire the actual sea environment parameters and provide them to the simulator, which can improve the authenticity of the simulation system. Furthermore, three MAC protocols, Aloha-CS, CSMA/CA, and DACAP, are simulated on the integrated simulation system under the same condition including ocean environment, network topology, and parameters. The results show that the performance of CSMA/CA is greater than the other protocols in such networks. It also proves that the integrated simulation system can accurately simulate the relevant characteristics of the MAC protocol.

Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network

The prediction of underwater acoustic transmission loss in the sea plays a key role in generating situational awareness in complex naval battles and assisting underwater operations. However, the traditional classical underwater acoustic transmission loss models do not consider the regional hydrological elements, and the performance of underwater acoustic transmission loss prediction under complex environmental conditions in a wide range of sea areas is limited. In order to solve this problem, we propose a deep learning-based underwater acoustic transmission loss prediction method. First, we studied the application domains of typical underwater acoustic transmission loss models (ray model, normal model, fast field program model, parabolic equation model), analyzed the constraint rules of its characteristic parameters, and constructed a dataset according to the rules. Then, according to the characteristics of the dataset, we built a DBN (deep belief net) neural network model and used DBN to train and learn the dataset. Through the DBN method, the adaptation and calculation of the underwater acoustic transmission loss model under different regional hydrological elements were carried out in a simulation environment. Finally, the new method was verified with the measured transmission loss data of acoustic sea trials in a certain sea area. The results show that the RMSE error between the underwater acoustic transmission loss calculated by the new method and the measured data was less than 6.5 dB, the accuracy was higher than that of the traditional method, and the prediction speed was faster, the result was more accurate, and had a wide range of adaptability in complex seas.

Environment-aware communication channel quality prediction for underwater acoustic transmissions: A machine learning method

Due to the limited energy supply of sensor nodes in underwater acoustic communication networks (UACNs), energy optimization for underwater acoustic transmissions is critical to prolong the network lifetime and improve network performance. Machine learning is a powerful and promising method that can be used to optimize energy consumption of UACNs. In this paper, we propose a machine learning based environment-aware communication channel quality prediction (ML-ECQP) method for UACNs. In ML-ECQP, the logistic regression (LR) algorithm is used to predict the communication channel quality (which is measured according to the bit error rate) between a transmitter and a receiver based on the perceived underwater acoustic channel environmental parameters (such as signal-to-noise ratio, underwater temperature, wind speed, etc.). Based on the predicted communication quality, each transmitter can optimize the acoustic data transmissions in order to minimize the energy waste caused by retransmissions, thus significantly reducing the energy consumption of UACNs. Extensive experiments are conducted in the Furong Lake at Xiamen University to demonstrate the performance (in terms of the feasibility, channel condition predication accuracy, and energy consumption reduction) of the proposed ML-ECQP method.

Adaptive Coding and Bit-Power Loading Algorithms for Underwater Acoustic Transmissions

Underwater acoustic channel (UAC) is featured as fast time-varying characteristic, and challenges the transmission designs. To countermine the time variation effect, we propose an adaptive design for orthogonal frequency division multiplexing (OFDM) transmission systems by utilizing the long-term stability of the second-order statistics of the channel state information (CSI). We derive the analytical expression of signal-to-interference-plus-noise-ratio (SINR) at each subcarrier to reach the target error performance based on the statistical information of the CSI. Thereafter, a new adaptive coding and bit-power loading algorithm with low computational complexity is proposed to pursuit the highest achievable bit rate with fixed error rate. The validity of SINR calculations and the effectiveness of the proposed adaptive algorithm are demonstrated under various conditions, wherein both simulated and measured channels have been tested.

Low bit rate underwater video image compression and coding method based on wavelet decomposition

In view of the limited bandwidth of underwater video image transmission, a low bit rate underwater video compression coding method is proposed. Based on the preprocessing process of wavelet transform and coefficient down-sampling, the visual redundancy of underwater image is removed and the computational coefficients and coding bits are reduced. At the same time, combined with multi-level wavelet decomposition, inter frame motion compensation, entropy coding and other methods, according to the characteristics of different types of frame image data, reduce the number of calculations and improve the coding efficiency. The experimental results show that the reconstructed image quality can meet the visual requirements, and the average compression ratio of underwater video can meet the requirements of underwater acoustic channel transmission rate.