## 3 Types of Underwater Communication Systems
### Underwater Wireless Communication
DL is a subset of ML that uses artificial neural networks with multiple layers to learn complex patterns from large amounts of data [28]. In the context of underwater communication, sound sources are classified as natural and artificial or anthropogenic [2; 28]. Natural sources include fish, marine mammals, and crabs (underwater living organisms, as a whole) [1; 2; 3; 28], while artificial sources refer to sounds from ships, machinery, and human communication systems [1; 2; 28]. Sound waves are longitudinal, meaning that the vibrations of the medium occur parallel to the direction of the wave, resulting in regions of compression and rarefaction within the medium, and this process generates pressure that negatively impacts the morph-physiology of fish and invertebrates [1; 3]. There are two methods of SONAR detection in use today: active and passive [93]. Passive sonar refers to the sensing and collection of acoustic signals emitted by entities or sources in the marine environment, such as ships, fish, and other sea creatures [2; 62; 93]. The detection of target objects, such as marine lifeforms and other underwater events using monitoring systems is essential to understand the aquatic environments and their characteristics. UWAC is an established field [91] that employs advanced technologies today. Its applications vary depending on objectives and areas of interest, including military and civilian communications, biological research, underwater resource detection, and bathymetry, among others [62; 91]. Consequently, communication interferences between UWAC and sonar detection systems have been reported [94]. In contrast, active sonar involves the emission of a sound wave by an observer, which then detects the echo of the emitted signal to identify the target that reflected the signal sent from the observer's device [28; 93]. Active SONAR, or active acoustics, works similarly to LiDAR and Radio Detection and Ranging (RADAR), but uses sound waves instead of light or radio waves, involving transmitting sound waves into the water column and analysing the echoes that return [28]. The active acoustics method enables precise mapping of sea floors, scanning underwater landscapes, and detecting objects and lifeforms [28; 92]. To improve the accuracy of detecting and interpreting reflected signals, ML and DL techniques have become increasingly important [2; 28; 92; 93]. By training these models with extensive data, systems can more effectively differentiate between target signals and environmental noise, leading to more reliable detection [2; 28; 92; 93]. However, since active detection relies on sending a signal and receiving its reflection, errors can occur when the marine environment generates signals similar to those of the target [92]. This overlap makes distinguishing between them challenging, even with matched filters, potentially reducing detection accuracy [92]. The Doppler effect is another factor that can compromise the integrity of the radiated signal from a target [62; 94]. The effect occurs when the distance between the sound source and the observer changes, resulting in a shift in frequency as perceived by the observer [62; 94]. In underwater environments, the Doppler effect is commonly encountered due to the presence of numerous moving sound-emitting sources. [PERSON] et al. [94] describe two methods for correcting the Doppler effect: one method involves extracting Doppler coefficients by estimating the frequency change of a clockwise impulse signal using a notch filter [94], while the other method estimates the chirp rate change of a Linear Frequency-Modulated (LFM) signal using the Fractional Fourier Transform (FRFT) [94]. By correcting the Doppler effect, signals of interest can be accurately identified, thereby reducing detection errors. UEFC is a novel method for underwater communication systems that utilises Electric Field (EF) radiation [75].