# High-Speed Underwater Optical Wireless Communication with Advanced Signal Processing Methods Survey
## 5 Discussion and Future Scope  
Although a promising solution, UOWC systems face the key challenge of ISI caused by signal scattering in underwater channels. To improve the data transmission distance and improve the BER, various types of equalizers have been proposed and studied, as discussed in detail in Section 3. Both linear and nonlinear equalizers have been investigated, and better BER and higher data rates in UOWC systems have been achieved. However, linear equalizers have the limitations of only being capable of stable communication channels. When the channel is disturbed, the performance of the linear equalizer is degraded significantly. In addition, they cannot suppress the nonlinear effects that widely exist in UOWC systems. On the other hand, nonlinear equalizers can suppress various types of nonlinearities in UOWC systems, and hence, have enhanced capability to improve data transmission performance. However, they normally have complex equalizer structures. Although the nonlinear equalizer configurations can be adjusted according to the influence of non-linearity, the variable underwater channel environment limits equalizer adjustment efficiency.  
With the advancement of artificial intelligence, ML equalizers have been studied as well and have shown unique advantages over traditional NML equalizers. They can learn from the received signal to equalize signals in different underwater channels, noises, and system geometric arrangements. In particular, ML equalizers have enhanced capability in suppressing nonlinear effects in UOWC systems. ML equalizers can also handle multiple impairments simultaneously, effectively solving the interactions among different impairments. However, ML equalizers also face a number of key challenges. More complicated algorithms are typically required in complex environments, and these algorithms require a large number of training datasets and iterations, which often take a long time and are computationally expensive, requiring high-end hardware. Some recent research [113; 114; 115] starts to optimize the algorithms and reduce the training epochs to reduce the memory and time cost of ML equalizers. Solving this challenge becomes one focus of future UOWC post-signal processing technology.  
Future underwater communication applications will certainly rely on reliable and powerful signal processing techniques to improve the system BER and performance. Adaptive equalizations can be a promising future research direction. For instance, in a calm lake or clear ocean, which has a stable underwater channel with limited nonlinear effect, a low-cost and simple linear equalizer can operate efficiently. In windy and choppy seas, nonlinear equalizers can be employed to reduce nonlinear effects and improve system performance. Finally, in challenging underwater channels such as turbidity and harbor water, variable impurities and underwater turbulence cause substantial changes in signal transmission. Traditional NML equalizers have limited capability in such complex and variable underwater channels. Instead, ML equalizers can automatically learn according to different channel performances and system configurations to optimize the performance of equalizing signals. However, since complex algorithms lead to a long training time and a larger number of training iterations, developing efficient hardware accelerators in ML-based equalizers can also be a future research field. Furthermore, so far the signal processing is mostly considered in point-to-point direct links. With the relay concept being considered in UOWC networks, the corresponding signal processing technique can be another research direction.