Search Results (324)

The rapid advancement of underwater wireless communication technologies is critical to unlocking the full potential of marine resource exploration and environmental monitoring. This paper reviews recent progress in three primary modalities: underwater acoustic communication, radio frequency (RF) communication, and underwater optical wireless communication (UWOC), each designed to address specific challenges posed by complex underwater environments. Acoustic communication, while effective for long-range transmission, is constrained by ambient noise and high latency; recent innovations in noise reduction and data rate enhancement have notably improved its reliability. RF communication offers high-speed, short-range capabilities in shallow waters, but still faces challenges in hardware miniaturization and accurate channel modeling. UWOC has emerged as a promising solution, enabling multi-gigabit data rates over medium distances through advanced modulation techniques and turbulence mitigation. Additionally, bio-inspired approaches such as electric field communication provide energy-efficient and robust alternatives under turbid conditions. This paper further examines the practical integration of these technologies in underwater platforms, including autonomous underwater vehicles (AUVs), highlighting trade-offs between energy efficiency, system complexity, and communication performance. By synthesizing recent advancements, this review outlines the advantages and limitations of current underwater communication methods and their real-world applications, offering insights to guide the future development of underwater communication systems for robotic and vehicular platforms. Full article

Underwater sensor networks (UWSNs) play a crucial role in subsea operations like marine exploration and environmental monitoring. A major challenge for UWSNs is achieving effective and energy-efficient data collection, particularly in deep-sea mining, where energy limitations and long-term deployment are key concerns. This study introduces a Channel-Aware AUV-Aided Data Collection Scheme (CADC) that utilizes deep reinforcement learning (DRL) to improve data collection efficiency. It features an innovative underwater node traversal algorithm that accounts for unique underwater signal propagation characteristics, along with a DRL-based path planning approach to mitigate propagation losses and enhance data energy efficiency. CADC achieves a 71.2% increase in energy efficiency compared to existing clustering methods and shows a 0.08% improvement over the Deep Deterministic Policy Gradient (DDPG), with a 2.3% faster convergence than the Twin Delayed DDPG (TD3), and reduces energy cost to only 22.2% of that required by the TSP-based baseline. By combining a channel-aware traversal with adaptive DRL navigation, CADC effectively optimizes data collection and energy consumption in underwater environments. Full article

Underwater acoustic (UWA) communication supports many critical applications but still faces several physical-layer signal processing challenges. In response, recent advances in machine learning (ML) and deep learning (DL) offer promising solutions to improve signal detection, modulation adaptability, and classification accuracy. These developments highlight the need for a systematic evaluation to compare various ML/DL models and assess their performance across diverse underwater conditions. However, most existing reviews on ML/DL-based UWA communication focus on isolated approaches rather than integrated system-level perspectives, which limits cross-domain insights and reduces their relevance to practical underwater deployments. Consequently, this systematic literature review (SLR) synthesizes 43 studies (2020–2025) on ML and DL approaches for UWA communication, covering channel estimation, adaptive modulation, and modulation recognition across both single- and multi-carrier systems. The findings reveal that models such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and generative adversarial networks (GANs) enhance channel estimation performance, achieving error reductions and bit error rate (BER) gains ranging from ${10}^{-3}$ to ${10}^{-6}$. Adaptive modulation techniques incorporating support vector machines (SVMs), CNNs, and reinforcement learning (RL) attain classification accuracies exceeding 98% and throughput improvements of up to 25%. For modulation recognition, architectures like sequence CNNs, residual networks, and hybrid convolutional–recurrent models achieve up to 99.38% accuracy with latency below 10 ms. These performance metrics underscore the viability of ML/DL-based solutions in optimizing physical-layer tasks for real-world UWA deployments. Finally, the SLR identifies key challenges in UWA communication, including high complexity, limited data, fragmented performance metrics, deployment realities, energy constraints and poor scalability. It also outlines future directions like lightweight models, physics-informed learning, advanced RL strategies, intelligent resource allocation, and robust feature fusion to build reliable and intelligent underwater systems. Full article

Communication in deep-sea shadow zones remains a significant challenge due to high propagation losses, complex multipath effects, long transmission delays, and strong environmental influences. In recent years, orthogonal chirp division multiplexing (OCDM) has demonstrated promising performance in underwater acoustic communication due to its robustness against multipath interference. However, its high peak-to-average power ratio (PAPR) limits its reliability and efficiency in deep-sea shadow-zone environments. This study applies a recently proposed generalized orthogonal chirp division multiplexing (GOCDM) modulation scheme to deep-sea shadow-zone communication. GOCDM follows the same principles as orthogonal signal division multiplexing (OSDM) while offering the advantage of a reduced PAPR. By segmenting the data signal into multiple vector blocks, GOCDM enables flexible resource allocation, optimizing the PAPR without compromising performance. Theoretical analysis and practical simulations confirm that GOCDM preserves the full frequency diversity benefits of traditional OCDM, while mitigating PARR-related limitations. Additionally, deep-sea experiments were carried out to evaluate the practical performance of GOCDM in shadow-zone environments. The experimental results demonstrate that GOCDM achieves superior performance under low signal-to-noise ratio (SNR) conditions, where the system attains a 0 bit error rate (BER) at 4.2 dB and 6.8 dB, making it a promising solution for enhancing underwater acoustic communication in challenging deep-sea environments. Full article

The underwater acoustic (UWA) communication system faces challenges due to environmental factors, extensive multipath spread, and rapidly changing propagation conditions. Deep learning based solutions, especially for orthogonal frequency division multiplexing (OFDM) receivers, have been shown to improve performance. However, the UWA channel characteristics are highly dynamic and depend on the specific underwater conditions. Therefore, these models suffer from model mismatch when deployed in environments different from those used for training, leading to performance degradation and requiring costly, time-consuming retraining. To address these issues, we propose a transfer learning (TL)-based pre-trained model for OFDM based UWA communication. Rather than training separate models for each underwater channel, we aggregate received signals from five distinct WATERMARK channels, across varying signal to noise ratios (SNRs), into a unified dataset. This diverse training set enables the model to generalize across various underwater conditions, ensuring robust performance without extensive retraining. We evaluate the pre-trained model using real-world data from Qingdao Lake in Hangzhou, China, which serves as the target environment. Our experiments show that the model adapts well to these challenging environment, overcoming model mismatch and minimizing computational costs. The proposed TL-based OFDM receiver outperforms traditional methods in terms of bit error rate (BER) and other evaluation metrics. It demonstrates strong adaptability to varying channel conditions. This includes scenarios where training and testing occur on the same channel, under channel mismatch, and with or without fine-tuning on target data. At 10 dB SNR, it achieves an approximately 80% improvement in BER compared to other methods. Full article

The Automatic Modulation Classification (AMC) for underwater acoustic signals enables more efficient utilization of the acoustic spectrum. Deep learning techniques significantly improve classification performance. Hence, they can be applied in AMC work to improve the underwater acoustic (UWA) communication. This paper is based on the adoption of Hough Transform (HT) and Edge Detection (ED) to enhance modulation classification, especially for a small dataset. Deep neural models based on basic Convolutional Neural Network (CNN), Visual Geometry Group-16 (VGG-16), and VGG-19 trained on constellation diagrams transformed using HT are adopted. The objective is to extract features from constellation diagrams projected onto the Hough space. In addition, we use Orthogonal Frequency Division Multiplexing (OFDM) technology, which is frequently utilized in UWA systems because of its ability to avoid multipath fading and enhance spectrum utilization. We use an OFDM system with the Discrete Cosine Transform (DCT), Cyclic Prefix (CP), and equalization over the UWA communication channel under the effect of estimation errors. Seven modulation types are considered for classification, including Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) (2/8/16-PSK and 4/8/16/32-QAM), with a Signal-to-Noise Ratio (SNR) ranging from −5 to 25 dB. Simulation results indicate that our CNN model with HT and ED at perfect channel estimation, achieves a 94% classification accuracy at 10 dB SNR, outperforming benchmark models by approximately 40%. Full article

This study investigates the combined effects of mesoscale eddies and rough sea surfaces on acoustic propagation in the eastern Arabian Sea and Gulf of Aden during summer monsoon conditions. Utilizing three-dimensional sound speed fields derived from CMEMS data, sea surface spectra from the SWAN wave model validated by Jason-3 altimetry, and the BELLHOP ray-tracing model, we quantify their synergistic impacts on underwater sound. A Monte Carlo-based dynamic sea surface roughness model is integrated with BELLHOP to analyze multiphysics interactions. The results reveal that sea surface roughness significantly influences surface duct propagation, increasing transmission loss by approximately 20 dB compared to a smooth sea surface, while mesoscale eddies deepen the surface duct and widen convergence zones by up to 5 km. In deeper waters, eddies shift convergence zones and reduce peak sound intensity in the deep sound channel. These findings enhance sonar performance and underwater communication in dynamic, monsoon-influenced marine environments. Full article

Unmanned Underwater Vehicles (UUVs) play an irreplaceable role in marine exploration, environmental monitoring, and national defense. The UUV depends on underwater acoustic communication (UAC) technology to enable reliable data transmission and support efficient collaboration. As the complexity of UUV missions has increased, secure UAC has become a critical element in ensuring successful mission execution. However, underwater channels are inherently characterized by high error rates, limited bandwidth, and signal interference. These problems severely limit the efficacy of traditional security methods and expose UUVs to the risk of data theft and signaling attacks. Cryptography-based security methods are important means to protect data, effectively balancing security requirements and resource constraints. They provide technical support for UUVs to build secure communication. This paper systematically reviews key advances in cryptography-based secure UAC technologies, focusing on three main areas: (1) efficient authentication protocols, (2) lightweight cryptographic algorithms, and (3) fast cryptographic synchronization algorithms. By comparing the performance boundaries and application scenarios of various technologies, we discuss the current challenges and critical issues in underwater secure communication. Finally, we explore future research directions, aiming to provide theoretical references and technical insights for the further development of secure UAC technologies for UUVs. Full article

Traditional underwater acoustic reconnaissance technologies are limited in directly detecting underwater acoustic communication signals. This paper proposes a dual-feature ResNet–Transformer model with two innovative breakthroughs: (1) A dual-modal fusion architecture of ResNet and Transformer is constructed using residual connections to alleviate gradient degradation in deep networks and combining multi-head self-attention to enhance long-distance dependency modeling. (2) The time–frequency representation obtained from the smooth pseudo-Wigner–Ville distribution is used as the first input branch, and higher-order statistics are introduced as the second input branch to enhance phase feature extraction and cope with channel interference. Experiments on the Danjiangkou measured dataset show that the model improves the accuracy by 6.67% compared with the existing Convolutional Neural Network (CNN)–Transformer model in long-distance ranges, providing an efficient solution for modulation recognition in complex underwater acoustic environments. Full article

Accurate acoustic identification of marine mammals is vital for monitoring ocean health and human impacts. Existing methods often struggle with limited single-feature representations or suboptimal fusion of multiple features. This paper proposes an Evaluation-Adaptive Weighted Multi-Head Fusion Network that integrates CQT and STFT features via a dual-branch ResNet architecture. The model enhances intra-branch features using channel attention and adaptive weighting of each branch based on its validation accuracy during training. Experiments on the Watkins Marine Mammal Sound Database show that the proposed method achieves superior performance, reaching 96.05% accuracy and outperforming baseline and attention-based fusion models. This approach offers an effective solution for multi-feature acoustic recognition in complex underwater environments. Full article

By transferring external information between the equalizer and the decoder iteratively, the performance of turbo equalization is close to the channel capacity. Conventional turbo equalization (CTE) relies on channel coding, while the transmission of external information is a problem in an uncoded system, and turbo equalization without channel coding (TECC) remains unexplored. Therefore, this paper introduces a TECC framework with a virtual decoder constructed by bidirectional processing. The main innovation is that the existence of the virtual decoder enables the transmission of external information. Under this new framework, we implement it with a minimum mean square error decision feedback equalizer (MMSE-DFE) and evaluate its performance across stationary channels and multipath fading channels. Simulation results demonstrate significant communication performance enhancement after three to four iterations, surpassing both conventional bidirectional and unidirectional equalization. In addition, the proposed TECC is verified through underwater acoustic communication in a sea trial. The results also demonstrate that the TECC achieves better bit error performance. Full article

For a bottom-moored vertical line array in deep ocean, the underwater maneuvering source will produce interference patterns in both grazing angle–distance (vertical-time record, VTR) and frequency–grazing angle (wideband beamforming output) domains, respectively, and the interference period is modulated by the source depth. Based on these characteristics, an interference feature fusion (IFF) method is proposed in the space–time–frequency domain for source depth estimation, in which the principal interference mode of the VTR is extracted adaptively and the depth ambiguity function is constructed by fusing the ambiguity sequence, mapped by wideband beamforming intensity, and the principal interference mode, which can achieve the long-term depth estimation and recognition of underwater sources without requiring environmental information. Theoretical analysis and simulation results indicate that the IFF can suppress the false peaks generated by the generalized Fourier transform (GFT) method, and the depth estimation error of the IFF for a single source is reduced by at least 47% compared to GFT. In addition, the IFF is proven to be effective at separating the depth of multiple adjacent sources (with the average estimation error reduced by 28%) and exhibits a high degree of robustness within the fluctuating acoustic channel (with the average estimation error reduced by 12%). Full article

In underwater signal processing, accurate time delay estimation (TDE) is of crucial importance for ensuring the reliability of data transmission. However, the complex propagation of sound waves and strong noise interference in the underwater environment make this task extremely challenging. Especially under the condition of low signal-to-noise ratio (SNR), the existing methods based on cross-correlation and deep learning struggle to meet requirements. Aiming at this core issue, this paper proposed an innovative solution. Firstly, a multi-sub-window reconstruction is performed on the frequency-sliding generalized colorboxpinkcross-correlation (FS-GCC) matrix between signals to capture the time delay characteristics from different frequency bands and conduct the enhancement and extraction of features. Then, the grayscale image corresponding to the generated FS-GCC matrix is used, and the multi-level noise features are extracted by the multi-layer convolution of denoising convolutional neural network (DnCNN), effectively suppressing the noise and improving the estimation accuracy. Finally, the model-agnostic meta-learning (MAML) framework is introduced. Through training tasks under various SNR conditions, the model is enabled to possess the ability to quickly adapt to new environments, and it can achieve the desired estimation accuracy even when the number of underwater training samples is limited. Simulation validation was conducted under the NOF and NCS underwater acoustic channels, and results demonstrate that our proposed approach exhibits lower estimation errors and greater stability compared with existing methods under the same conditions. This method enhances the practicality and robustness of the model in complex underwater environments, providing strong support for the efficient and stable operation of underwater sensor networks. Full article

In order to improve the efficiency of the deep network model in processing the radiated noise signals of underwater acoustic targets, this paper introduces a Singular Spectrum Analysis and Channel Attention Convolutional Neural Network (SSA-CACNN) model. The front end of the model is designed as an SSA filter, and its input is the time-domain signal that has undergone simple preprocessing. The SSA method is utilized to separate the noise efficiently and reliably from useful signals. The first three orders of useful signals are then fed into the CACNN model, which has a convolutional layer set up at the beginning of the model to further remove noise from the signal. Then, the attention of the model to the feature signal channels is enhanced through the combination of multiple groups of convolutional operations and the channel attention mechanism, which facilitates the model’s ability to discern the essential characteristics of the underwater acoustic signals and improve the target recognition rate. Experimental Results: The signal reconstructed by the first three-order waveforms at the front end of the SSA-CACNN model proposed in this paper can retain most of the features of the target. In the experimental verification using the ShipsEar dataset, the model achieved a recognition accuracy of 98.64%. The model’s parameter count of 0.26 M was notably lower than that of other comparable deep models, indicating a more efficient use of resources. Additionally, the SSA-CACNN model had a certain degree of robustness to noise, with a correct recognition rate of 84.61% maintained when the signal-to-noise ratio (SNR) was −10 dB. Finally, the pre-trained SSA-CACNN model on the ShipsEar dataset was transferred to the DeepShip dataset with a recognition accuracy of 94.98%. Full article

The underwater acoustic (UWA) communication system is characterized by limited bandwidth, while continuous phase modulation (CPM) offers a constant envelope, improving power and spectrum utilization efficiency. However, severe inter-symbol interference (ISI) poses a significant challenge in CPM-based UWA communication. Traditional CPM frequency domain equalization (FDE) combined with simple phase detection neglects the inherent coding gain from CPM, leading to performance degradation. Although Viterbi detection provides high performance, its complexity makes it unsuitable for computationally constrained UWA systems. This paper proposes a symbol detection algorithm based on posterior probabilities combined with FDE (PS-FDE). PS-FDE improves CPM signal detection performance by effectively separating information, applying delay, and performing multiple rounds of information merging. Simulations using minimum shift keying (MSK) and Gaussian MSK signals demonstrate significant performance improvement in just a few iterations over UWA channels. A sea trial further validates the algorithm, showing a 15.83% reduction in bit error rate after three information mergings. Full article