# High-Speed Underwater Optical Wireless Communication with Advanced Signal Processing Methods Survey
## 2 Overview of UOWC Systems
### Signal Processing Techniques  
As mentioned in Figure 3, when light passes through water, some of the light is scattered and travels in other directions. Generally, the Henyey-Greenstein (HG) function and the two-term Henyey-Greenstein (TTHG) function [97] are widely used to represent the scattering phase function (SPF) in UOWC systems [8; 98], which are widely used in the Monte Carlo (MC) simulation of UOWC systems [80; 99; 100; 101; 102]. Due to the scattering in the underwater channel, the signal photons arrive at the detector through different optical paths, leading to a delay in the time-of-arrival and ISI, which degrades the signal quality and reduces the transmission data rate. Since scattering is determined by the size and density of particles in water, ISI has little impact on deep sea or clear ocean UOWC systems. However, in harbor water and coastal water, the performance of UOWC systems is highly affected by ISI [8; 39; 103]. In most practical UOWC applications, eliminating the ISI at the receiver is not trivial because the channel response is not accurately known. The simplest and most common signal-processing technique to suppress the impact of ISI is the linear equalizer. Among them, zero-forcing linear equalizer (ZF-LE), which equalizes the folded spectrum of the received signal using a filter with the inverse frequency response, has been applied in many UOWC studies [37; 104; 105]. The ZF-LE can eliminate the ISI at the sampling time to increase the system SNR. However, the ZF-LE cannot be used when the folded spectrum has nulls, which can cause infinite noise enhancement. To avoid this, the mean-square error linear equalizer (MSE-LE) has been studied. Unlike the ZF-LE eliminating the ISI, the MSE-LE passes a small part of ISI to the output to improve the SNR and BER in UOWC systems [31; 106]. It needs to be mentioned that we only give a snapshot here and more details of these works are presented later. Even though linear equalizers are easy to operate in systems, the infinite noise enhancement of ZF-LE with spectral nulls and large noise enhancement of MSE-LE with deep attenuation in the passband limit their applications. To overcome these issues, more complex and advanced nonlinear equalizers have been further studied in UOWC systems. One of the most common nonlinear equalizers in UOWC systems is the zero-forcing decision-feedback equalizer (ZF-DFE), which is designed to cancel ISI and completely avoid infinite noise enhancement by employing a whitened matched filter. To further improve the performance, the mean-square error decision-feedback equalizer (MSE-DFE) was investigated, which uses a linear predictor and a feedback filter to whiten the noise at the output. Due to its complex structure and design, MSE-DFE is rarely used in UOWC systems but is  
Figure 4: UOWC channel configurations. widely used in UAC systems [107; 108; 109]. In summary, signal processing techniques that rely on non-machine learning equalizers are widely used in UOWC systems. In Section 3, we review the recent studies of non-machine learning equalization in UOWC systems. With the development of neural networks and artificial intelligence, ML applications based on neural networks have been widely investigated in UOWC systems to enhance performance. In general, there are four types of machine learning algorithms: supervised, unsupervised, semi-supervised, and reinforcement learning. The most commonly used ML algorithm in the UOWC system is supervised learning, which uses known output features to derive computational relationships between input training data and output data [110]. The supervised ML method has been widely applied in signal processing to suppress various impairments in the UOWC system, such as improve the BER and enhance stability [50; 111; 112; 113; 114; 115]. The encoder/decoder based on supervised ML algorithms has also been studied to improve the data transmission in recent UOWC research [116; 117].