Underwater Acoustic Communication Research Articles

Reinforcement learning-based automated modulation switching algorithm for an enhanced underwater acoustic communication

Acoustic communication is the preferred method for underwater communication due to its superior propagation characteristics, including longer range, lower attenuation, better penetration, and adaptability to water density. However, challenges like varying water depths, temperature gradients, noise, and multipath propagation require innovative solutions beyond fixed-data rate systems and single modulation schemes. To address these challenges, we propose a reinforcement learning (RL)-based automated modulation switching algorithm designed to enhance data transmission efficiency. This approach leverages the UNETStack software and the Frequency Hopping-Binary Frequency Shift Key (FH-BFSK) modulation, extending its capabilities by incorporating Amplitude Shift Keying (ASK), Phase Shift Keying (PSK), and Orthogonal Frequency Division Multiplexing (OFDM) modulation techniques. We designed a cost-effective, software-controlled acoustic modem using commercial off-the-shelf (COTS) components, enabling full-duplex underwater communication. The RL algorithm utilizes a Q-matrix guided by a greedy policy to assess network conditions, such as data volume and data rates. It continuously monitors underwater environments to select the most suitable modulation scheme dynamically. Experimental validation demonstrates a 3.648 % improvement in Received Signal Strength Indicator (RSSI), a 32 % reduction in bit error rate at a constant 7 dB Signal-to-Noise Ratio (SNR), and a 5 % increase in utility at 10 dB SNR when using OFDM selected by the reinforcement learning algorithm, compared to FH-BFSK.

A Heterogeneous Glider System with Underwater Acoustic Communication and Positioning

Abstract Autonomous underwater vehicles are increasingly frequently used in the fields of ocean observation, data collection, and tactical surveillance. Underwater navigation and near real-time communication are the most important factors limiting the performance of these vehicles. In this article, an underwater acoustic communication and positioning system (UACPS) based on heterogeneous gliders is presented. The system consists of an acoustic wave glider and an acoustic underwater glider, and includes an ultrashort baseline and an acoustic modem. The program and communication protocol of the communication and positioning algorithm are independently developed, and the hardware circuit design of the communication and ultra-short baseline positioning, as well as the embedded surface wave gliders and gliders are independently implemented. Sea trial results indicate that the communication distance of the system is more than 3 km and the positioning error is less than 5%. With the further improvement of system performance, the low cost, long time, long distance and near real-time communication characteristics of our UACPS can be used to integrate multiple autonomous unmanned platforms as part of intelligent surveillance robot networks.

Interception of Bio-Mimicking Underwater Acoustic Communications Signals

Interception of Bio-Mimicking Underwater Acoustic Communications Signals

SSL-LRN: A Lightweight Semi-Supervised-Learning-Based Approach for UWA Modulation Recognition

Due to the lack of sufficient valid labeled data and severe channel fading, the recognition of various underwater acoustic (UWA) communication modulation types still faces significant challenges. In this paper, we propose a lightweight UWA communication type recognition network based on semi-supervised learning, named the SSL-LRN. In the SSL-LRN, a mean teacher–student mechanism is developed to improve learning performance by averaging the weights of multiple models, thereby improving recognition accuracy for insufficiently labeled data. The SSL-LRN employs techniques such as quantization and small convolutional kernels to reduce floating-point operations (FLOPs), enabling its deployment on underwater mobile nodes. To mitigate the performance loss caused by quantization, the SSL-LRN adopts a channel expansion module to optimize the neuron distribution. It also employs an attention mechanism to enhance the recognition robustness for frequency-selective-fading channels. Pool and lake experiments demonstrate that the framework effectively recognizes most modulation types, achieving a more than 5% increase in recognition accuracy at a 0 dB signal-to-noise ratio (SNRs) while reducing FLOPs by 84.9% compared with baseline algorithms. Even with only 10% labeled data, the performance of the SSL-LRN approaches that of the fully supervised LRN algorithm.

Multi-task recognition of modulation types and arrival directions of underwater acoustic signals based on convolutional neural networks

Abstract Nowadays, underwater acoustic communication reconnaissance technology is of great importance in military, marine science, and resource exploration fields. The fields involve military reconnaissance and intelligence collection, ocean monitoring and security, marine scientific research, and resource exploration. This article mainly focuses on the identification of modulation types and emission angles contained in underwater acoustic signals. Convolutional Neural Networks (CNNs) are used for deep learning to train, validate, and obtain final results on signal data. This network achieves the independent tasks of automatic modulation classification and Direction of Arrival (DOA) estimation of radio signals simultaneously. The model achieves efficient identification and filtering by designing different filter sizes and sizes, as well as different Y-shaped connection structures. It includes one input block and two output blocks, and the two output blocks correspond to the outputs of modulation classification and DOA estimation. At the same time, based on this, the parameters of the network layer are modified to achieve the generalization ability of the neural network. The training and validation data of the network consists of simulation data from underwater acoustic communication emissions and real data from the Songhua Lake experiment in Jilin Province, China, to test the network recognition efficiency.

Localization and detection of underwater acoustic communication signals using convolutional recursive neural networks

Abstract This paper employs a multi-task neural network (UWAELD) to simultaneously perform the Underwater Acoustic Communication Event Detection (UACED) and direction-of-arrival (DOA) mission. Typically, the UACED and DOA tasks are executed separately, as the UACED task is identified through time-frequency analysis, while the DOA task is identified through the amplitude and phase differences of the signal, making it very difficult to jointly perform both tasks. To overcome this, we utilize a logarithmic spectrogram for time-frequency analysis and stack the normalized eigenvectors obtained by processing the signal covariance matrix. We ultimately evaluate the network’s feasibility using simulation data and lake test data, utilizing acoustical signals measured in actual experiments. Finally, for the UADCED task, the error rate is only 6%, with an F1 score of 0.83; for the DOA task, the localization error is only 1.72°, with a localization recall score of 0.87. Additionally, for the DOA task, we employ the classical MUSIC algorithm to demonstrate the network’s superiority.

Adaptive Modulation and Coding for Underwater Acoustic OTFS Communications Based on Meta-Learning

Adaptive Modulation and Coding for Underwater Acoustic OTFS Communications Based on Meta-Learning

An Optimized CNN-Attention Network for Clipped OFDM Receiver of Underwater Acoustic Communications

An Optimized CNN-Attention Network for Clipped OFDM Receiver of Underwater Acoustic Communications

One2ThreeNet: An Automatic Microscale-Based Modulation Recognition Method for Underwater Acoustic Communication Systems

One2ThreeNet: An Automatic Microscale-Based Modulation Recognition Method for Underwater Acoustic Communication Systems

High covertness camouflage covert underwater acoustic communication based on masking technique

In marine military operations, it is very important to ensure the safe transmission of underwater information, so it is urgent to improve the covertness of underwater communication. In order to meet the requirement of high covertness in underwater acoustic communication, according to the natural characteristics of superimposed interference of marine ambient noise, a high covertness camouflage covert underwater acoustic communication (CUWAC) method based on masking technique using ship-radiated noise as communication carrier is proposed in this paper. This method leverages innovative audio digital technology to embed information into the DCT domain of ship-radiated noise, creating a disguised signal that is hard to detect, thereby significantly enhancing stealth without compromising communication effectiveness. In order to improve the demodulation performance of this camouflage CUWAC, this paper proposes a variable step size p-norm-like LMP sparse adaptive filtering algorithm to offset the interference of the masking signal at the receiving end. This adaptive filtering algorithm is based on the improved modified Gaussian function to construct step size. Furthermore, it introduces the error signal autocorrelation function to adjust the step size and zero-attracting term dynamically. The proposed adaptive filtering algorithm obtains excellent anti-noise performance. Through a series of simulation experiments, this paper not only validates the feasibility of the CUWAC method but also demonstrates its stability and reliability under various marine environmental conditions, providing robust theoretical support for practical applications, proving its efficiency and reliability in complex marine environments.

Modulation recognition for underwater acoustic communication based on hybrid neural network and feature fusion

It is a huge challenge for underwater acoustic receivers to correctly identify modulation methods due to the complex underwater channel environment and severe noise interference. Combined with the lightweight network (SqueezeNet) and attention mechanism (SENet), a multi-attribute and multi-scale feature fusion model based on a hybrid neural network is proposed, which achieves efficient and accurate recognition for modulation modes. First, the wavelet time-frequency (WTF) spectrum, square power spectrum, and contour maps of cyclic spectrum are extracted as multi-attribute inputs for the network to reduce the impact of inherent defects in single attribute feature. Second, shallow and deep features based on the SqueezeNet model are obtained as multi-scale features, of which the key feature expression ability is enhanced by the SENet model to provide sufficient feature information for modulation recognition. The simulation experiments and sea trial data confirm that the suggested method demonstrates strong generalization capabilities and effectiveness when applied to underwater acoustic channels and environmental noise. In contrast to algorithms in existence, the method verifies superior recognition abilities.

Modulation recognition of underwater acoustic communication signals based on neural architecture search

Modulation recognition of underwater communication signals is a critical aspect of underwater information confrontation. However, the current deep learning-based methods for underwater communication modulation recognition often mimic neural network architectures used in image processing and speech processing, tending to perform poorly in low signal-to-noise ratio (SNR) conditions. This paper introduces a novel approach by utilizing neural architecture search (NAS) method. By automatically searching for neural architectures that are well-suited for modulation recognition in underwater environment, our proposed method improves the classification performance particularly in low SNR conditions. Furthermore, we have also proposed a recognition method based on attention mechanism and feature fusion, which substantially enhances the accuracy of identifying phase-modulated signals. Numerical simulation and experimental data are used to demonstrate performance of proposed methods.

Delay-aware and reliable medium access control protocols for UWSNs: Features, protocols, and classification

Underwater Wireless Sensor Networks (UWSNs) are garnering significant interest for their broad potential in critical applications, including environmental monitoring, resource exploration, and disaster prevention. These networks, however, face unique challenges due to the complexities of underwater acoustic communication, such as extended propagation delays and environmental disturbances. These issues necessitate the development of sophisticated Medium Access Control (MAC) protocols specifically designed for UWSNs, distinct from those used in Terrestrial Wireless Sensor Networks (TWSNs). This paper provides a comprehensive review of MAC protocols tailored for various task-oriented scenarios in UWSNs, beginning with an in-depth exploration of the challenges specific to designing MAC protocols for these networks. The discussion emphasizes the importance of minimizing transmission latency and maximizing reliability, crucial for effective application-specific operations. This survey categorizes existing protocols based on these critical performance metrics, while also addressing the imperative of energy efficiency. Furthermore, it details the adaptive strategies these protocols employ to cope with the dynamic and challenging underwater acoustic channels. In addition to presenting a comparative analysis of the protocols, this work identifies pressing research gaps and poses open questions, establishing itself as an indispensable resource for researchers seeking to advance the field of MAC protocols in UWSNs.

Direction of Arrival Joint Prediction of Underwater Acoustic Communication Signals Using Faster R-CNN and Frequency–Azimuth Spectrum

Utilizing hydrophone arrays for detecting underwater acoustic communication (UWAC) signals leverages spatial information to enhance detection efficiency and expand the perceptual range. This study redefines the task of UWAC signal detection as an object detection problem within the frequency–azimuth (FRAZ) spectrum. Employing Faster R-CNN as a signal detector, the proposed method facilitates the joint prediction of UWAC signals, including estimates of the number of sources, modulation type, frequency band, and direction of arrival (DOA). The proposed method extracts precise frequency and DOA features of the signals without requiring prior knowledge of the number of signals or frequency bands. Instead, it extracts these features jointly during training and applies them to perform joint predictions during testing. Numerical studies demonstrate that the proposed method consistently outperforms existing techniques across all signal-to-noise ratios (SNRs), particularly excelling in low SNRs. It achieves a detection F1 score of 0.96 at an SNR of −15 dB. We further verified its performance under varying modulation types, numbers of sources, grating lobe interference, strong signal interference, and array structure parameters. Furthermore, the practicality and robustness of our approach were evaluated in lake-based UWAC experiments, and the model trained solely on simulated signals performed competitively in the trials.

Improving the efficiency of channel evaluation during high-speed data transmission in underwater acoustic communication with OFDM

Problem statement. The organization of noise-resistant wireless underwater acoustic communication is not a simple and solved task. One of the main problems is the multipath propagation of a sound acoustic wave as a result of re–reflections from the seabed surface and a change in the frequency of signal vibrations when the receiver and transmitter move relative to each other. Thus, finding a channel evaluation solution that can improve the quality of communication is an urgent task for developers of underwater acoustic communication systems. Purpose. To consider the possibility of developing effective methods for processing underwater acoustic communication signals with high noise immunity by evaluating the communication channel. Results. The possibility of using an adaptive filter with a sliding window to evaluate the channel during high-speed data transmission in underwater acoustic communication is shown. The simulation results showed that the proposed adaptive filter with a sliding window surpasses the normalized adaptive filters in terms of the BER bit error and wins over additive filters with an exponential window in reducing the number of mathematical operations. When modeling wireless acoustic communication systems with OFDM, error-free data transmission with a spectral efficiency of 0.33 bits/s/Hz was achieved. Practical significance. The obtained results can be used to improve the wireless transmission of data in underwater acoustic communication due to the proposed algorithm of the adaptive filter.

A Throughput Performance Analysis Method for Multimode Underwater Acoustic Communication Network Based on Markov Decision Process

The multimode underwater acoustic communication network is a novel form of underwater acoustic communication that adjusts its communication mode to enhance overall performance. Current performance analysis methods are primarily applied to single-mode networks and assume uniform communication capability across all nodes, making them unsuitable for multimode networks. This paper investigates the multimode communication of the physical layer, considering factors such as the marine environment, the node transmitting sound source level, and the transmitting distance. A decoding conflict model is proposed to support multimode concurrent transmission scenarios. The communication mode is designed to be compatible with the channel and node characteristics. Additionally, using a Markov decision process, this paper establishes a performance evaluation and analysis model for multimode underwater acoustic networks to determine throughput performance limits in real underwater environments. Simulations across various scenarios validate that the throughput performance limits obtained by this method are more accurate under multimode networks, with an improvement in accuracy of over 67.5% compared to existing methods.

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.

Low Computational Complexity RLS-Based Decision-Feedback Equalization in Underwater Acoustic Communications

Low Computational Complexity RLS-Based Decision-Feedback Equalization in Underwater Acoustic Communications

A novel physical layer authentication mechanism for static and mobile 3D underwater acoustic communication networks

In this paper, we present an innovative physical layer authentication approach for underwater acoustic communication networks. Our method leverages the transmitter nodes’ positions to establish a robust authentication mechanism. We consider two scenarios based on the mobility of the nodes. In the first scenario, underwater nodes (both legitimate and malicious) are static, while the second scenario assumes that the underwater nodes are mobile moving at a certain velocity. For both scenarios, we estimate position by analyzing the signals received at reference nodes that are strategically placed within a predetermined underwater area. Once the estimates are available, we propose binary hypothesis testing based on the estimated position to determine the legitimacy of the transmitter node. Furthermore, when the nodes are mobile, we perform velocity estimation at a certain time by taking the difference of the estimated coordinates, for which we also find the uncertainty in the estimation. We use a linear Kalman filter operation to track the legitimate node’s mobility. We provide closed-form expressions of the false alarm rate and missed detection rate resulting from binary hypothesis testing. We validate our proposed mechanism through simulation, demonstrating error behavior against link quality, malicious node location, and receiver operating characteristic (ROC) curves.

Strategic Game Model for AUV-Assisted Underwater Acoustic Covert Communication in Ocean Internet of Things

Strategic Game Model for AUV-Assisted Underwater Acoustic Covert Communication in Ocean Internet of Things