# High-Speed Underwater Optical Wireless Communication with Advanced Signal Processing Methods Survey
## 4 Machine Learning Applications in UOWC
### Supervised Learning in UOWC Systems
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Bit Rate (pps)**} & \multirow{2}{*}{**Distance**} & **Optical** & \multirow{2}{*}{**Receiver**} & **Transmitter** & **Modulation** & \multirow{2}{*}{**ML Algorithm**} & \multirow{2}{*}{**Refs.**} \\  & & & **Source** & & **Power** & & **Scheme** & \\ \hline
2018 & 1.5 G & 1.2 m Tap & 457 nm LED & PIN PD & N/A & PAM8 & GK-DNN & [50] \\
2019 & N/A & 1 m Turbid & 532 nm LD & CCD & \(\sim\)20 mW & N/A & CNN & [116] \\
2020 & 3.2 G & 1.2 m Tap & Blue LED & PIN PD & N/A & 64-QAM & DBMLP & [111] \\
2020 & N/A & 1.2 m Tap & Blue LED & PIN PD & N/A & 64-QAM & TFDNet & [112] \\
2021 & N/A & 4.3 m Turbid & 632.8 m LD & Camera & \(\sim\)2 mW & N/A & CNN & [117] \\
2021 & 2.85 G & 1.2 m Tap & Blue LED & PIN PD & N/A & 64-QAM & PCVNN & [113] \\
2021 & N/A & 30 m Coastal & Blue LED & PD & N/A & ÖAM & BDNet & [105] \\
2021 & 3.1 G & 1.2 m Tap & Blue LED & PINPD & \(\sim\)10 mW & 64-QAM & TL-DBMLPs & [114] \\
2022 & 1 M & 1.5 m Tap & 450 nm LED & SPAD & \(\sim\)1 W & OOK & DNN & [127] \\
2023 & 660 M & 90 m Tap & 450 nm LD & PMT & \(\sim\)188.8 mW & I-SC-FDM & SWI-DNN & [115] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recent research progress in the UOWC system based on supervised ML. Figure 9: Structure of the GK-DNN [50]. smaller number of training epochs. Experimental results show that the proposed UOWC system employing TL-DBMLPs can reduce the size of the training set from 50% to 10% of the total dataset to achieve an acceptable mean square error (MSE). Moreover, with only 10 epochs, the achievable BER is improved from \(1\times 10^{-2}\) with conventional DBMLPs to \(1\times 10^{-3}\) using the TL-DBMLPs. Moreover, a novel sparse weight-initiated deep neural network (SWI-DNN) equalizer combined with the interleaved single-carrier frequency division multiplexing (I-SC-FDM) scheme was employed for UOWC systems in [115]. Due to the implementation of a special SWI structure, the necessary training epochs of the SWI-DNN equalizer can be reduced by 10.3%. Results show that to achieve the BER of \(3.8\times 10^{-3}\) (i.e., FEC limit) in the 90 m UOWC system, the data rate can be increased by 17.9% after employing the ML equalizer than conventional TFD equalizers. In addition, a 16-ary orbital angular momentum shift keying (OAM-SK) equalizer based on convolutional neural networks (CNNs) was proposed in [116]. Results show that ML schemes can achieve an accuracy of more than 96% and a larger number of pixels in the camera receiver can be utilized in a camera-based UOWC system. Hence, the ML-based equalizer can significantly improve the accuracy of data decoding. In addition, the other equalizer based on CNNs was employed in [117]. They succeed in achieving a high accuracy (93.7\(\sim\)99.9%) in high-turbulence UOWC with a camera-based receiver for image transmission. A larger alphabet and faster classification rates can be achieved in LD-based UOWC systems. In [105], a new equalizer based on a blind detection network (BDNet) was considered in UOWC systems. Unlike the previous blind channel estimation (BCE) schemes, the BDNet has the advantage of estimating the inverse channel regardless of the scalar ambiguity issue by learning the latent channel features from the received signal only. Simulation results show that the proposed UOWC with the BDNet achieves better BER performance compared with conventional BCE schemes. In addition, ML algorithms have also been studied for time synchronization and clock recovery in UOWC systems. The recovery of the time slot synchronous in the photon-counting UOWC system is critical, and conventionally it is realized based on symbol synchronization and frame synchronization, which has limited accuracy. To predict the phase value of the time slot synchronous clock, a method of time slot synchronous clock recovery for photon-counting UOWC based on DNNs is designed in [127].