Relationship between Signal Retransmission Interval and Communication Quality in Underwater Acoustic Communication Using Time-Diversity and Orthogonal Signal Division Multiplexing

Convolutional autoencoders for low probability of detection constrained underwater acoustic communications

Tsunamis and submarine earthquakes pose severe risks to coastal regions, demanding rapid and reliable monitoring systems. While the Deep-ocean Assessment and Reporting of Tsunamis (DART) system has been globally deployed, its dependence on pressure sensors and one-to-one communication limits its applicability to the Korean East Sea. This paper introduces the Korean Tsunami and Earthquake Monitoring System, which integrates seafloor seismometers and proposes a dedicated Medium Access Control (MAC) protocol optimized for multi-node underwater acoustic communication. The study performs a comprehensive analytical derivation of closed-form expressions for channel utilization and energy consumption under diverse node configurations and acoustic conditions. The analytical results confirm that the proposed MAC protocol maintains stable performance, supports multi-node operation, and enables long-term monitoring within the limited energy budget of underwater devices. The derived results also provide practical design implications for underwater network planning, including guidelines on node placement, frame duration, and control packet timing for efficient data delivery. Although empirical validation remains as future work, the findings establish theoretical benchmarks and engineering insights for the design of next-generation underwater monitoring systems tailored to Korean coastal environments.

This study investigates the dynamic formation reconfiguration problem for multi-UUV (multi-Unmanned Underwater Vehicle) systems, with a particular focus on the challenges posed by underwater acoustic communication. A two-dimensional grid model is established in the horizontal plane, taking the leader vehicle as a reference point. Based on this model, fundamental motion strategies for formation reconfiguration are proposed. To facilitate reconfiguration, the Particle Swarm Optimization (PSO) algorithm is utilized to assign desired position points to the follower UUVs within the new formation, enabling dynamic target point planning during reconfiguration. Furthermore, the process of generating motion guidance commands and the impact of acoustic communication delays during command transmission are analyzed. To address these delays, a fuzzy logic-based delay compensation method is proposed. Simulation experiments were conducted to validate the proposed approach. The results demonstrate that the formation reconfiguration planning method and the centralized command communication compensation strategy are both effective and practical for multi-UUV systems.

Orthogonal frequency division multiplexing (OFDM) is a promising solution for underwater acoustic communication (UWA); however, it requires careful handling of the challenges of large multipath and severe Doppler effects inherent in underwater acoustic communication. This paper proposes a novel feedforward backpropagated neural network (FBNN) implementation for Doppler scaling estimation using UWA cyclic-prefix (CP) OFDM communication. A two-layered input-output feedforward network is utilized with three different backpropagated training algorithm variants: Fletcher-Reeves Conjugate Gradient (CGF), Polak-Ribiére Conjugate Gradient (CGP), and Conjugate Gradient with Powell/Beale Restarts (CGB). The proposed approach calculates the Doppler scale factor by combining the neural computational power with the accuracies offered by the three training algorithms. To evaluate the effectiveness of the proposed FBNN implementation, root mean square error (RMSE) is used as a performance metric for different multipath and signal-to-noise ratio (SNR) channel conditions. The paper also presents a comparison of the proposed FBNN-based training algorithms’ performance with that of the benchmark offered by conventional methods.

Fast Time-Varying Sparsity Exploitation With Adaptive Observation Length Strategies in Underwater Acoustic Communication

Block Decision Feedback Equalization for OSDM in Underwater Acoustic Communications

Aiming at the problem of strong-power self-interference (SI) in in-band full-duplex (FD) underwater acoustic communication (UWAC) systems, in this paper, we propose an advanced self-interference cancellation (SIC) method that makes use of the differing channel characteristics of two hydrophones. The method effectively cancels the SI signal at the more distant receiver by using the hydrophone closer to the near-end source as a reference. Our approach can dynamically update the channel parameters of the SIC model to maintain stable communication performance. To validate this design, results from both pool and sea-trial experiments are presented. The pool experiments demonstrate approximately 54.75 dB of SIC performance. The bit error rate of the far-end signal remains below 1 ×10-3. In the sea trials, shallow-sea communication was achieved across 50 km with a signal-to-interference ratio of -40 dB. Compared with conventional SIC methods, the proposed method offers stronger SIC capability, particularly when handling nonlinear interference. Furthermore, it can adapt to unknown sources of interference. The results indicate that our method provides a promising solution for SI cancellation in FD-UWAC systems, providing significant theoretical and practical value.

Monte Carlo-constructed polar codes with embedded pilots for enhanced underwater acoustic communication

Low complexity equalizer with iterative interference cancellation for OTFS underwater acoustic communications

Discrete Hartley transform - orthogonal frequency division multiplexing underwater acoustic communication system based on complex-to-real conversion

Channel polarization and application of OFDM underwater acoustic communication

Introduction Underwater acoustic (UWA) communication systems confront significant challenges due to the unique, dynamic, and unpredictable nature of acoustic channels, which are impacted by low signal-to-noise ratio (SNR), severe multipath propagation, latency, Doppler spread, and a shortage of real-world data. Orthogonal frequency division multiplexing (OFDM) is essential for establishing resilient and reliable data transmission in these challenging environments, but accurate channel estimation remains a critical barrier to unlocking its full potential—especially given the limitations of conventional estimation methods in adapting to UWA channel dynamics. Methods This work introduces a Convolution-Recurrent Neural Network (CRNet) estimator integrated with dynamic signal decomposition (DSD) techniques (e.g., Local Mean Decomposition, LMD; Empirical Mode Decomposition, EMD) to estimate UWA-OFDM channel characteristics and mitigate noise-induced distortions in received signals. The CRNet architecture combines convolutional layers (to capture spatial features) and recurrent layers (to model temporal dependencies), enabling it to learn complex UWA channel dynamics. The model is trained using paired data: received pilot symbols, transmitted pilots, and accurate channel impulse responses (CIR). Post-training, CRNet operates using only the received signal as input, eliminating the need for supplementary channel characteristics like SNR. To ensure real-world relevance, training and testing datasets are generated via the Bellhop ray-tracing model, which simulates diverse UWA environments (shallow coastal and continental shelf). Results Numerical findings demonstrate that the proposed CRNet model consistently outperforms benchmark methods—including least squares (LS), minimal mean square error (MMSE), and backpropagation neural network (BPNN)—across key metrics: bit error rate (BER), amplitude error, and phase error. CRNet exhibits superior performance with QPSK modulation compared to QAM, and maintains robustness even with a small number of pilot symbols. Performance evaluations on both training and unseen datasets confirm its resilience and flexibility in demanding UWA environments, validating its ability to generalize to dynamic channel conditions beyond training scenarios. Discussion The CRNet estimator addresses critical limitations of conventional UWA-OFDM channel estimation methods: its dual focus on spatial and temporal features (via convolutional-recurrent layers) overcomes the static linear constraints of LS/MMSE, while DSD-driven noise mitigation enhances input signal quality for more accurate estimation. By eliminating reliance on post-training supplementary channel data (e.g., SNR), CRNet simplifies real-world deployment. Its superior BER performance and adaptability to diverse UWA environments (shallow coastal, continental shelf) position it as a robust solution for improving the reliability and efficiency of UWA communication systems.

Along with the rapid development of underwater craft such as autonomous underwater vehicles and remotely operated vehicles, underwater wireless communication is a rapidly growing field. We developed an underwater wireless optical communication system; the divergence angle and communication rate of the system can be adjusted to adapt to different communication conditions. We also integrate the underwater acoustic communication and ranging module in the system for interaction. The innovative use of acoustic signals to interact with states and instructions is proposed, which is convenient for adaptive parameter adjustment to achieve optical alignment and establish the high-speed communication link. We have realized the functional verification in the water tank for what we believe to be the first time. Limited by the maximum diagonal length of 22 m of the water tank, the system can transmit an equivalent 66 m distance in Jerlov I water, with a highest communication rate of nearly 100 Mbps. It is seamlessly connected to the traditional 100 Mbps Ethernet without protocol adaptation. The results of this paper have important reference value for promoting the practical application of underwater optical communication.

Considering that multi-band interference often leads to a significant increase in the bit error rate at the system receiver end in actual underwater acoustic communication environments, this paper proposes a subcarrier silence anti-interference technology scheme based on filter bank multi-carrier (FBMC) with index modulation (IM). First, it is analyzed that, under three different underwater acoustic channels and without added interference, the underwater acoustic filter bank multi-carrier with index modulation (FBMC-IM) communication system outperforms traditional FBMC systems in terms of bit error rate performance. Subsequently, targeting the frequency distribution characteristics of multi-band interference, this paper designs an adaptive subcarrier silence mechanism. Through notch detection, interference band information is fed back to the transmitter, and subcarriers within the communication band that overlap with the interference signal spectrum are silenced, while unaffected subcarriers continue to carry communication information, thereby achieving multi-band partitioning to avoid interference effects. Additionally, to further enhance system performance, the paper integrates Virtual Time Reversal Mirror (VTRM) channel equalization technology, which leverages the time-focusing characteristics of multipath signals to effectively suppress multipath interference and delay spread in the acoustic channel. Simulation and field test results demonstrate that the proposed subcarrier-silence-based FBMC-IM anti-interference scheme significantly improves system reliability under multi-narrowband interference conditions. In the simulated underwater acoustic channel, the BER is reduced by approximately 65–80% at a signal-to-noise ratio of 0 dB; in the 5 km test channel in the Bohai Sea, the BER is reduced by 70–85% compared to the traditional FBMC system; in the test channel near Dalian with strong multipath spread, the BER is improved by more than one order of magnitude at a signal-to-noise ratio of 30 dB, with a BER reduction exceeding 90% under the configuration of Q = 4, k = 1. These results fully validate the superior anti-interference capability and communication robustness of the proposed scheme in interfering underwater acoustic environments.

Abstract Addressing the constraints imposed by the complexity of underwater acoustic channels on the cooperative communication performance of autonomous underwater vehicle (AUV) swarms, this paper constructs a cross-layer collaborative optimization framework based on Multi-Agent Reinforcement Learning and Federated Learning. By jointly optimizing the communication protocol stack and the distributed learning process, the system performance is enhanced. To support the efficient operation of this framework, two key technologies are proposed: (i) a Reinforcement Learning–based Dynamic Modulation Switching algorithm, which enhances channel adaptability through real-time optimization of physical layer parameters, and (ii) a Channel State Information–aware Lightweight Federated Learning mechanism, which utilizes dynamic compression techniques to reduce communication overhead and improve the efficiency of distributed training. These methods aim to overcome the challenges of underwater acoustic communication, achieving efficient, reliable, and intelligent cooperative operations for AUV swarms.

Key areas such as marine resource exploration, real-time monitoring of ecological environments, and national defense security systems urgently require reliable underwater information transmission capabilities as a foundation. Underwater acoustic communication (UAC), leveraging its unique advantages as the most effective method for long-range data transfer in aquatic environments, has become an indispensable enabling technology for supporting these core applications. This review systematically examines recent advancements in UAC technology and their critical role in enabling modern marine initiatives. The analysis covers key developments in both non-coherent and coherent communication systems, including single-carrier and multi-carrier modulation schemes such as OFDM. It highlights their respective advantages in terms of robustness and high-data-rate transmission. The significant impact of challenging underwater channel characteristics, notably severe multipath fading, time-varying Doppler shifts, limited bandwidth, and environmental noise, is discussed alongside corresponding mitigation strategies. Furthermore, the integration of machine learning for sophisticated channel estimation, adaptive equalization, and intelligent system optimization is explored as a promising frontier. Emerging technologies like spread-spectrum, full-duplex, and covert UAC are also evaluated for their potential in specialized and high-stakes applications. The paper concludes by identifying persistent challenges, including regulatory constraints, physical-layer security issues, interoperability across platforms, and energy efficiency demands. Finally, it outlines future research directions aimed at developing more intelligent, secure, and efficient next-generation underwater networks.

This paper proposes a frequency hopping binary frequency shift keying underwater acoustic (UWA) communication system, where a denoising diffusion probabilistic model (DDPM) and a convolutional neural network (CNN) are sequentially used for signal reconstruction and signal demodulation, respectively. Unlike the deep transfer learning (DTL)-based system, this system employs a DDPM to process the received Mel-spectrogram, reconstructing the distorted Mel-spectrogram caused by UWA channel effects, and generating a spectrogram that approximates the transmitted signal, which is then demodulated by the CNN. The proposed system outperforms conventional systems and achieves performance comparable to DTL-based systems in simulation and experiment. DTL requires data samples from new scenarios to learn signal characteristics during deployment; in contrast, this method uses the generative capability of DDPM to enable direct deployment in dynamic underwater environments without additional adaptation processes, offering flexibility and suitability for complex and variable UWA propagation channels.

Bio-inspired secure underwater acoustic communication based on cetacean clicks and chaotic sequences

Abstract Underwater acoustic communication (UAC) is a fundamental component of various applications, including marine exploration, underwater robotics, and environmental monitoring. However, the dynamic conditions of underwater environments pose significant challenges to the development of high-performance communication systems. This study introduces a novel dynamic adaptive modulation approach that enables smooth switching between several communication approaches using machine learning methods such as decision trees (DT) and random forests (RF). Through continuous monitoring of environmental parameters, such as temperature, salinity, depth, and acoustic noise levels, the system gathers real-time information to serve as input features for the machine learning model. The model is trained and evaluated to identify the most suitable modulation approach for the prevailing underwater conditions. The DT model achieved an accuracy of 97%, whereas the RF model performed slightly better, reaching 98% accuracy. In addition, the switching delay for the DT model was 2.75 ms, whereas that for RF was 0.97 ms. The approach improves effective data transmission by reducing error rates, enhancing system robustness, and dynamically adjusting to changing underwater conditions, thereby ensuring optimal communication efficiency in challenging environments.