# Recent Progress on Underwater Wireless Communication Methods and Applications
## 2 Main Methods of Underwater Wireless Communication
### Underwater Acoustic Communication
General adversarial networks (GANs), with their innovative adversarial training framework, have demonstrated remarkable potential in generating clean signals that closely mimic authentic acoustic patterns [4; 32; 33]. Beyond these core architectures, attention mechanisms have been seamlessly integrated to dynamically weigh critical signal components, thereby amplifying salient features while suppressing noise. Autoencoder frameworks, through their encoder-decoder architecture, have further advanced denoising by reconstructing signals with enhanced clarity, particularly in complex noise scenarios [4; 34; 35]. To bolster adaptability across diverse underwater environments, researchers have also incorporated data augmentation strategies. Techniques such as controlled noise addition and strategic masking operations have been systematically applied to diversify training datasets. These methods effectively expose models to a broader spectrum of noise conditions [36; 37; 38; 39], thereby boosting resilience and generalization without compromising the integrity of the underlying signal structure. However, despite these advancements, several challenges persist in practical implementation. The computational \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal Decomposition** & **Denoising** & **Grouping** & **Reference** \\ \hline DWT & Wavelet Thresholding & NA & [26] \\ EMD & Evolutionary filtering & Entropy & [6] \\ EMD & Wavelet Thresholding & Entropy & [27] \\ VMD & Wavelet Thresholding & Correlation & [1] \\ DWT, VMD & Wavelet Thresholding & NA & [28] \\ VMD & Discard noisy IMFs & Entropy & [29] \\ VMD & Grey relation analysis and thresholding & Frequency & [30] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of traditional denoising approaches based on signal decomposition. overhead associated with training and deploying large-scale neural networks remains a significant bottleneck, especially for resource-constrained underwater devices. Real-time processing requirements further complicate deployment, as latency-sensitive applications demand immediate signal fidelity. Additionally, the reliance on extensive labeled training data poses logistical hurdles in data-scarce underwater domains. These limitations have catalyzed growing interest in developing lightweight neural architectures optimized for energy efficiency and speed [38, 40], as well as self-supervised learning paradigms that minimize dependency on manual annotation [41, 42]. This evolving research trajectory reflects the field's commitment to balancing algorithmic sophistication with practical deployability in the demanding underwater communication landscape [31]. Additionally, the severely restricted bandwidth of underwater acoustic channels imposes a fundamental limitation on data transmission rates, making it inadequate for high-data-demand scenarios such as real-time video streaming or large-scale sensor networks. Unlike terrestrial systems, which leverage wide-frequency spectrums, underwater acoustic communication operates within a severely constrained frequency range. This inherent bandwidth limitation creates a trade-off between transmission rate, signal fidelity, and communication range [3, 47]. Furthermore, multipath propagation effects, caused by signal reflections off the seafloor and underwater obstacles, further complicate the signal reception process. These phenomena lead to severe intersymbol interference, signal distortion, and increased latency, thereby undermining the stability and speed of underwater communication links [48, 49]. Of equal importance to noise reduction is the enhancement of data transmission rates in underwater acoustic communication systems. These systems are inherently constrained by the limited bandwidth imposed by the physical properties of water, which significantly restricts their utility in scenarios demanding high data throughput, such as real-time video streaming or extensive sensor networks [3, 47, 48, 49].