Underwater Acoustic Communication Channel Research Articles

A metric to quantify the time-varying characteristics of underwater acoustic communication channels.

Underwater acoustic communication signals suffer from time dispersion due to time-varying multipath propagation in the ocean. This leads to intersymbol interference, which in turn degrades the performance of the communication system. Typically, the channel correlation functions are employed to describe these characteristics. In this paper, a metric called the channel average correlation coefficient (CACC) is proposed from the correlation function to quantify the time-varying characteristics. It has a theoretical negative relationship with communication performance. Comparative analysis involving simulations and experimental data processing highlights the superior effectiveness of CACC over the traditional metric, the channel coherence time.

Deep-Neural-Network-Based Receiver Design for Downlink Non-Orthogonal Multiple-Access Underwater Acoustic Communication

The excavation of the ocean has led to the submersion of numerous autonomous vehicles and sensors. Hence, there is a growing need for multi-user underwater acoustic communication. On the other hand, due to the limited bandwidth of the underwater acoustic channel, downlink non-orthogonal multiple access (NOMA) is one of the fundamental pieces of technology for solving the problem of limited bandwidth, and it is expected to be beneficial for many modern wireless underwater acoustic applications. NOMA downlink underwater acoustic communication (UWA) is accomplished by broadcasting data symbols from a source station to several users, which uses superimposed coding with variable power levels to enable detection through successive interference cancellation (SIC) receivers. Nevertheless, comprehensive information of the channel condition and channel state information (CSI) are both essential for SIC receivers, but they can be difficult to obtain, particularly in an underwater environment. To address this critical issue, this research proposes downlink underwater acoustic communication using a deep neural network utilizing a 1D convolution neural network (CNN). Two cases are considered for the proposed system in the first case: in the first case, two users with different power levels and distances from the transmitter employ BPSK and QPSK modulations to support multi-user communication, while, in the second case, three users employ BPSK modulation. Users far from the base station receive the most power. The base station uses superimposed coding. The BELLHOP ray-tracing algorithm is utilized to generate the training dataset with user depth and range modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. The DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in terms of BER in simulation results for all the modulation orders.

Reinforcement learning-based adaptive modulation scheme over underwater acoustic OFDM communication channels

The conventional adaptive modulation (AM) technology is restricted in its effectiveness within the rapidly changing time-space-frequency of the underwater acoustic (UWA) channel. In contrast, reinforcement learning (RL) based AM employs an online training mode that interacts with the channel environment, enabling optimal modulation and coding schemes. By utilizing channel information and considering the bit error ratio (BER) of signals, we propose an RL-based AM strategy that adapts the modulation scheme to enhance spectral efficiency in a non-stationary UWA environment and maximize data throughput for UWA communication. The research is centered on the application of multi-carrier modulation in orthogonal frequency division multiplexing (OFDM). The AM is formulated as a Markov decision process (MDP) and resolved through the implementation of the Proximal Policy Optimization (PPO) algorithm. Simulation experiments indicate that the PPO algorithm exhibits faster and more efficient convergence while maintaining a stable convergence effect, in comparison to the Deep Q-Network (DQN) algorithm, Double DQN (DDQN) algorithm, and Q-Learning (QL) algorithm when addressing the UWA modulation problem.

UCSNet: Priors Guided Adaptive Compressive Sensing Framework for Underwater Images

Image acquisition and reconstruction play an important role in underwater detections and explorations. However, the limited underwater acoustic communication channels and narrow bandwidth resources will have a great impact on the performance of the traditional data acquisition methods, resulting in the loss of details and blur in reconstructed underwater images. Compressive sensing theory (CS) which can reconstruct images from fewer measurement than that required by Nyquist sampling law has been proved to have good effect on image sampling and reconstruction. Nevertheless, the existing CS methods are not suitable for underwater images because most of them are designed for on-land images which have huge differences from underwater images. In this paper, we propose a novel priors guided adaptive underwater compressive sensing framework, dubbed UCSNet, which can effectively sample and reconstruct underwater images under a fixed low sampling ratio. In particular, our framework is composed of three sub-networks: underwater priors extraction and guidance network (UEGN), sampling matrix generation network (SMGNet) and channel-wise reconstruction network (CWRNet). Specifically, inspired by the underwater imaging physical models, UEGN is designed to extract features of underwater priors information and combine them adaptively. UEGN also introduce the imaging process into CS task to make sampling and reconstruction consistent with underwater imaging characteristics. SMGNet uses underwater content degradation to assist the analysis of structural information to generate sampling matrices. Considering the monotony of color tones caused by light absorption in underwater images, CWRNet embedded with the channel-wise module (CWM) is designed to enforce the whole network to allocate different number of sampling points on luminance and chrominance channels respectively and make feature maps extracted from them complement with each other. Experimental results demonstrate that our proposed framework can achieve both PSNR and SSIM gains on underwater images reconstruction quality and have greater visual quality than other state-of-art methods under fixed sampling ratios.

Characterization and modeling of high-speed underwater acoustic communication channels

High-speed mobile underwater acoustic communication (HSM-UAC) is a relatively unexplored terrain, but its importance is expected to increase with the widespread application of flexible underwater mobile platforms such as AUVs and UUVs. Knowledge of HSM-UAC channels is a prerequisite to achieving reliable communication. This work conducted two HSM-UAC channel measurement experiments with a maximum speed of 20knots. The analysis of channel time-varying characteristics demonstrates the shortcomings of traditional consistent Doppler frequency offset channel model. The experimental findings reveal the presence of two non-stationary modes in the HSM-UAC channel. For short moving distances, the changes in channel geometry result in each path experiencing a different Doppler scale, thus observing the multiscale-multilag (MSML) channels. For long moving distances, the alteration in sound ray propagation tracks entirely reshapes the channel impulse response (CIR) structure, leading to a rapid decline in channel time correlation. In further analysis, the correlation coefficient of the power delay profile (PDP) is used to measure channel stationarity. Subsequently, the stationary time and corresponding stationary distance are calculated as characterization parameters for the time-varying channel model. The quantitative evaluation results are helpful for the communication signal design and determining the update frequency of channel state information.

Experimental characterization of the mobile underwater acoustic communication channel in Shallow Water

This paper presents the results for the experimental characterization of the mobile underwater acoustic communication channel in shallow water through the statistics of the fading and the time and frequency dispersion parameters. Starting from measurements performed in Forno Bay, Arraial do Cabo, Rio de Janeiro, Brazil, using three hydrophones in the reception side, two CW signals (3 and 6 kHz) and three sequences of LFM chirps in different bands: 2 to 4 kHz, 5 to 7 kHz and 1 to 8 kHz, the mobile sounding was performed in that channel in order to characterize it as in narrowband as in wideband.

Precoded Faster-Than-Nyquist Signaling for Doubly Selective Underwater Acoustic Communication Channel

In this letter, we propose faster-than-Nyquist (FTN) signaling transmission that is robust for an underwater acoustic (UWA) doubly selective channel. Owning to the explicit benefits of a reduced symbol interval specific to FTN signaling, the detrimental effects of a time-selective channel are significantly relaxed. Furthermore, upon introducing single-carrier transmit precoding and equalization, the proposed FTN signaling achieves a high detection performance. Our simulation results demonstrate that the proposed FTN signaling outperforms the conventional Nyquist-based benchmark scheme in the doubly selective channel.

Localization and correction of location information for nodes in UWSN-LCLI

Location is essential to identify the floating senor nodes in underwater. Underwater Sensor Networks (UWSNs) enable various critical applications like monitoring underwater environment, flora and fauna, sea life, exploration such as earthquake, disaster mitigation, motion tracking. In many cases, an underwater sensor node is necessary. Some points make the underwater study different and slightly complex. The first point is that radio signal cannot detect the underwater environment, despite radio waves acoustic signals used, which runs due to water currents. The second point is that earthy networks are deployed in the 2D environment, but 3D node deployment is needed in the Underwater environment. The third important point is locating the sensor node position in 3D deployment because nodes move (Floating Nodes) with water waves. It is a very complex assumption to fix the location of the sensor node.Various locations finding algorithms are proposed for the earthy network. Still, the underwater acoustic communication channel's unique properties, like high and varying transmission delay, required the Algorithm to locate the node's position. Only a few localization algorithms are proposed and presented in recent years to address these location finding requirements. Existing algorithms are distributed, scalable, and iterative in nature, but at the same Time, these algorithms use many acknowledgement messages for location identification and communication. To reduce the number of messages and to overcome the transmission, a new Location Finding and Correcting Algorithm for underwater acoustic sensor networks has been proposed in this paper. Our proposed Algorithm (LCLI- Localization and Correction of Location Information for nodes in UWSNs) find the Location of nodes accurately and efficient by considering localization errors. In this paper, we have also proposed a location correcting Algorithm that may use when the localization errors are more. Our proposed Algorithm (UWSN-LCLI) is compared with existing proposed algorithms, and our Algorithm outperforms and shows significant improvement.

PERFORMANCE ENHANCEMENT OF UNDERWATER ACOUSTIC COMMUNICATION USING DEEP LEARNING APPROACH

This research aims to improve underwater acoustic communication using deep learning. Due to an increase in undersea operations, dependable communication systems have become more important. The undersea environments complexity reduces the efficacy of underwater audio communication, despite its widespread use. Using mathematical equations and approximations, the underwater sound pathway has been modeled. These projects aim to enhance underwater communication systems by better understanding the underwater audio channel. In this study, we investigate the abilities of device learning and deep studying to investigate and accurately replicate the underwater acoustic channel by making use of real-world underwater data. This is done by analyzing the results of the study. The information has been compiled with the aid of using a combination of strategies, which include machine learning and in-depth reading. In particular, the Deep Neural Community (DNN) and long quick term memory (LSTM) modeling strategies are used in order to achieve the goal of simulating the underwater audio channel. The results of the trials demonstrate that these models are capable of accurately modeling the underwater acoustic communication channel. Furthermore, the findings suggest that deep learning models, particularly LSTM, are better models in terms of mean absolute percentage error. The vast majority of the currently available UWSN routing protocols use a classical routing strategy.

HW/SW Platform for Measurement and Evaluation of Ultrasonic Underwater Communications.

The purpose of this work is to present a flexible system that supports the study of wideband underwater acoustic communications (UAC). It has been developed both to measure channels and to test transmission techniques under realistic conditions in the ultrasonic band. This platform consists of a hardware (HW) part that includes multiple hydrophones, projectors, analog front-ends, acquisition boards, and computers, and a software (SW) part for the generation, reception, and management of acoustic sounding signals and noise. UAC channels are among the most hostile ones and exhibit an important attenuation and distortion, essentially due to both multipath propagation, which results in a very long channel impulse response, and time-varying behavior, which produces a notable Doppler spread. To cope with this challenging medium, sophisticated transmission techniques must be employed. In this sense, adequate signal processing algorithms have been designed aiming not only at the analysis and characterization of underwater communication channels but also at the evaluation of diverse modulation, detection, and coding schemes, from Orthogonal Frequency Division Multiplexing (OFDM) to single-carrier digital modulations with a single-input multiple-output (SIMO) configuration that takes advantage of diversity techniques. Wideband sounding signals, to be injected into the sea from the transmitter side, are created with patterns that allow multiple tests on a batch. With offline processing of the captured data at the receiver side, different trials can be carried out in a very flexible manner. The different aspects of the platform are described in detail: the HW equipment used, the SW interface to control acquisition boards, and the signal processing algorithms to estimate the UAC channel response. The platform allows the analysis and design of new proposals for underwater communications systems that improve the performance of the current ones.

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

A traditional Viterbi decoder is primarily optimized for additive white Gaussian noise (AWGN). With the AWGN channel, it offers good decoding performance. However, the underwater acoustic communication (UAC) channel is extremely complicated. In addition to white noise, there are a variety of artificial and natural impulse noise that occur suddenly. The traditional Viterbi decoder cannot obtain the optimum performance under this case. In order to solve this problem, this paper introduces a novel Viterbi decoder with the impulsive noise, which is considered to be subjected to Middleton Class A distribution in shallow ocean. Since Middleton Class A noise is very complicated, a simplified model is first introduced. Then, the error analysis of simplified model under various parameters is discussed in detail. The analysis shows that the simplified one just leads to slight error. Hereafter, a novel Veterbi decoder using the simplified model is discussed. Compared to a traditional decoder, a preprocessing is just required. The performance of soft decision-based decoder in the Middleton Class A noise channel (MAIN) and AWGN are further compared. Based on our simulations, the new decoder can significantly improve the performance in comparison with conventional one, which further validates our presented method.

Multi-block Sparse Bayesian learning channel estimation for OFDM underwater acoustic communication based on fractional Fourier transform

In OFDM underwater acoustic communication, according to the sparse characteristics of underwater acoustic communication channel in the time domain, sparse Bayesian learning (SBL) is introduced to make the channel estimation. In order to reduce the use of pilots and improve the transmission efficiency, a sparse pilot structure is adopted, and a multi-blocks sparse Bayesian learning (MBSBL) channel estimation algorithm is proposed. Aiming at the problem of insufficient channel estimation of MBSBL algorithm in the condition of few pilots, the fractional Fourier transform is introduced to compensate the Doppler frequency shift, which further improves the channel estimation performance and output signal-to-noise ratio (SNR). Besides, the complexities of Orthogonal Matching Pursuit (OMP), SBL and MBSBL algorithms are summarized briefly. Simulation and sea trial data show that the proposed algorithm can save half the number of pilots and the channel estimation effect of proposed algorithm is better than that of OMP algorithm and SBL algorithm.

Partial Fractional Fourier Transform (PFrFT)-MIMO-OFDM for Known Underwater Acoustic Communication Channels

Communication over doubly selective channels (both time and frequency selective) suffers from significant intercarrier interference (ICI). This problem is severe in underwater acoustic communications. In this paper, a novel partial fractional (PFrFT)-MIMO-OFDM system is proposed and implemented to further mitigate ICI. A new iterative band minimum mean square error (BMMSE) weight combining based on LDLH factorization is used in a scenario of perfect knowledge of channel information. The proposed method is extended from SISO-OFDM configuration to MIMO-OFDM. Simulation results demonstrate that the proposed PFrFT-LDLH outperforms the other methods in the SISO-OFDM scenario and that its performance can be improved in MIMO-OFDM scenarios.

Study of probe signal bandwidth influence on estimation of coherence bandwidth for underwater acoustic communication channel

A signal transmitted in a shallow Underwater Acoustic Communication (UAC) channel suffers from time dispersion due to the multipath propagation and the refraction phenomena. This causes intersymbol interference of the received signal and frequency-selective fading observed in its spectrum. Coherence bandwidth is one of the key transmission parameters used for designing the physical layer of a data transmission system to minimise the influence of time dispersion on the received signal. It can be calculated on the basis of the channel impulse response, measured with the use of the correlation method and frequency modulated signals or pseudorandom binary sequences. Such signals have a narrow, impulse-like autocorrelation function if considered in baseband. However, in the case of bandpass measurements, the influence of the probe signal on the estimate of the impulse response, and thus on the estimate of transmission parameters, is no longer negligible. The paper presents the results of an experimental study on probe signal bandwidth influence on estimation of coherence bandwidth. Simulations were carried out using UAC channel impulse responses measured in an inland reservoir.

Analytical Performance Evaluation of GFDM in Underwater Acoustic Communication Systems

The rapid growth of the Internet of Things (IoT) has extended its concept to underwater environments. However, the implementation of these systems via wired communication still represents a technological challenge, mainly due to the high cost of their deployment. Therefore, wireless communications are seen as an interesting solution for the deployment of underwater communications systems. Preliminary research indicated that underwater acoustic wireless communication could be used for some Internet of Underwater Things applications, mainly due to the wide range of communications involved. However, a significant disadvantage of acoustic systems is their low transmission data rate; thus, studies and analyses to improve this disadvantage must be carried out. Considering that new waveforms have been proposed to improve the performance of terrestrial wireless communications systems, this work presents the development of general analytical expressions that allow the performance evaluation of the Generalized Frequency Division Multiplexing (GFDM) waveform in underwater environments. These analytical expressions were obtained considering a continuous-time model for the GFDM signal and modeling the underwater acoustic communication channel as a time-varying multipath channel. Numerical results were obtained for many different systems and channel parameters, allowing a quantitative evaluation of the system performance degradation.

Impacts of channel fluctuations on least-squares channel estimation in underwater acoustic communications.

The time-varying multipath introduces major distortions to transmissions in the underwater acoustic communication channel. Channel estimation is often used as one of the central steps to address such distortions in high-rate communication receivers. The focus of this paper is to quantify the impacts of the channel fluctuations on the performance of the least-squares channel estimator. A metric, channel variation ratio (CVR), is defined to describe the rate of fluctuations in the channel impulse responses. Equations are derived to reveal the direct relationships between the CVR and channel estimation performance, which is measured by the channel estimation mean squared error (MSE) and signal prediction error (SPE). The equations show that both the MSE and SPE increase linearly with the CVR. The MSE and SPE metrics both have an error floor for time-varying impulse responses, even with zero ambient noise. It is confirmed that an optimum estimated channel length, achieving the minimum estimation error, exists for time-varying impulse responses. The truncation effects in the channel estimation are also investigated. Experimental data are used to validate the findings.

Study of the Performance of DSSS UAC System Depending on the System Bandwidth and the Spreading Sequence.

A signal transmitted in an Underwater Acoustic Communication (UAC) system operating in a shallow-water channel suffers from strong time dispersion due to multipath propagation. This causes the Inter-Symbol Interference (ISI) observed in the received signal, which significantly limits the communication system’s reliability and transmission rate. In such propagation conditions, the Direct-Sequence Spread Spectrum (DSSS) method is one of the solutions that make reliable data transmission possible. In systems with one-to-one communication, it ensures communication with a satisfactory Bit Error Rate (BER). Additionally, it makes it possible to implement the Code-Division Multiple Access (CDMA) protocol in underwater acoustic networks. This paper presents the results of simulation and experimental communication tests on a DSSS-based UAC system using three types of spreading sequence, namely m-sequences, Kasami codes and Gold codes, and occupying different bandwidths from 1 kHz to 8 kHz around a carrier frequency equal to 30 kHz. The UAC channel was simulated by impulse responses calculated by the virtual sources method and the UAC chanel models available in the Watermark simulator. The experimental tests were conducted in a model pool. Based on the obtained results, a transmission rate was estimated, which is possible to achieve in strong multipath propagation conditions, assuming reliability expressed as BER less than 0.001.

Impact of Equalizer step size in Underwater Acoustic Communication Channel

The Underwater Acoustic Channel (UAC) is a time variant channel and its multipath effects create ISI. This is one of the most important obstacles in the UAC channel which reduces the transmission rate. To remove this obstacle, a proper filter has to be designed in the receiver section. In this article, optimal step size for equalizer is computed and compared the results with the known techniques namely Decision Feedback Equalizer with interleave division multiple access (DFE IDMA) and Cyclic Prefix - Orthogonal Frequency Division Multiplexing (CP-OFDM) Equalizer. Channels are modeled using ray tracing methods. The various factors considered are ambient noise, attenuation loss, bottom and surface loss. The overall path loss for channels is computed by summing up the attenuation loss, surface and bottom loss. Simulation results evident that for short range UAC channel, the BER in the order of 10-2 is achieved using proposed methodology with least Eb/No compared to standard DFE method.

Design of Algorithms and Protocols for Underwater Acoustic Wireless Sensor Networks

Nowadays, with the recent advances of wireless underwater communication and acoustic sensor devices technology, we are witnessing a surge in the exploration and exploitation of the ocean’s abundant natural resources. Accordingly, to fulfill the requirements of the exploration of the ocean, researchers have focused their work toward the design of methods and algorithms for the underwater acoustic sensor networks (UASNs). Although considerable research effort has been devoted to the development of a variety of UASN-based applications, very limited work has addressed the algorithmic design and analysis for UASN. To this end, we propose to provide a comprehensive design, development, and analysis of algorithms and protocols for UASNs. We discuss each of the fundamental UASN building blocks, such as (i) underwater acoustic communication channel modeling, (ii) sustainable coverage and target detection, (iii) Medium Access Control (MAC-layer design and time synchronization, (iv) localization algorithms design, and (v) underwater routing protocol. Then, we illustrate the different protocols from each category and compare their benefits and drawbacks. Finally, we discuss a few potential directions for future research related to the design of future generations of UASNs.

6G Wireless Channel Measurements and Models: Trends and Challenges

In this article, we present our vision of the application scenarios, performance metrics, and potential key technologies of 6G wireless communication networks. We then comprehensively survey 6G wireless channel measurements, characteristics, and models for all frequency bands and all scenarios, focusing on millimeter-wave (mm-wave), terahertz, and optical wireless communication channels under all spectra; satellite, unmanned aerial vehicle (UAV), maritime, and underwater acoustic communication channels under global coverage scenarios; and high-speed train (HST), vehicle-to-vehicle (V2V), ultra-massive multiple-input, multiple-output (MIMO), orbital angular momentum (OAM), and industry Internet of Things (IoT) communication channels under full application scenarios. We also provide future research challenges of 6G channel measurements, a general standard 6G channel model framework, and models for intelligent reflection surface (IRS)-based 6G technologies and artificial intelligence (AI)-enabled channel measurements and models.