# Recent Progress on Underwater Wireless Communication Methods and Applications
## 2 Main Methods of Underwater Wireless Communication
### Underwater Acoustic Communication  
Underwater acoustic communication, as one of the three primary communication methods for underwater environments, has significant advantages but also notable limitations. Its strengths include long propagation distances and effective performance in complex underwater scenarios, making it indispensable for submarine communications, marine monitoring, and underwater robotics. For instance, underwater acoustic communication is essential for maintaining communication links between surface stations and submerged platforms such as AUVs, allowing them to transmit real-time data for navigation, monitoring, and status updates. Additionally, acoustic waves can propagate over large distances, which makes this method suitable for deep-sea exploration where other communication methods such as radio-frequency signals would be ineffective [19]. However, it also suffers from critical drawbacks such as high noise levels, limited data transmission rates, and significant latency, primarily due to the challenging underwater environment and the nature of acoustic wave propagation. The complexity of water bodies--changes in salinity, temperature, and pressure--can affect signal attenuation, leading to signal distortion and long-distance loss. The wavelength of sound waves can also affect transmission distance, as shown in Figure 2. These factors further complicate underwater communication systems [20]. Figure 1: A simple structure of underwater wireless communication system. Figure 2: The correlation between transmission distance and acoustic wavelength in underwater acoustic communication. In recent years, researchers have directed significant attention toward tackling these limitations. Notably, high noise levels persist as a substantial obstacle, often obscuring vital signal components and undermining the reliability of underwater communication systems. The underwater medium is inherently noisy, with ocean currents, marine life activity, and anthropogenic interference constituting primary contributors to the acoustic noise spectrum. This complex acoustic backdrop can substantially degrade the fidelity of transmitted signals [21], introducing distortions that frequently intertwine with the desired signal, particularly in high-traffic maritime zones or regions of intense biological activity. Moreover, the challenge of disentangling noise-induced artifacts from legitimate signal components intensifies in such environments. To counteract these effects and enhance signal intelligibility [22], extensive research efforts have been channeled into advancing denoising methodologies and refining acoustic signal recognition algorithms. Researchers have explored a wide array of techniques, including conventional linear filtering approaches, adaptive wavelet transforms, empirical mode decomposition (EMD), and its more sophisticated iteration, variational mode decomposition (VMD) [23; 24; 25]. Table 1 provides a summary of traditional denoising approaches. While these traditional frameworks establish a foundational framework for noise mitigation, their efficacy in adapting to the temporal and spatial variability of dynamic underwater acoustic environments remains constrained [26]. Recent advancements have therefore shifted toward hybrid intelligent signal processing architectures and data-driven modeling techniques to address these shortcomings. To further enhance denoising performance, deep learning (DL) approaches have been introduced as promising paradigms [5; 31]. Table 2 shows basic information of deep learning-based denoising approaches. Specifically, convolutional neural networks (CNNs) have been employed for their exceptional feature extraction capabilities from spatial data, while recurrent neural networks (RNNs) excel in modeling temporal dependencies inherent in time-series acoustic signals.