## 2 Proof-of-Concept Experiment and Algorithm  
Figure 1(a) shows the experimental setup of the wide FOV air-to-water OCC system using CuDNNLSTM with light emitting diode (LED) panel Tx and mobile phone CMOS  
camera for underwater Rx. The authors of [32] discussed optical Tx designs for maintaining signal integrity, and the authors of [33] discussed sensor performance under dynamic conditions. The mobile phone used in the experiment was an Asus(r) Zenfone(r) 3, having 16 MP, resolution of 1920 \(\times\) 1440 pixels, frame rate of 30 frames per second (fps) and shutter speed of 1/10,000. The mobile phone was placed inside a waterproof transparent case, and immersed in the water to a depth of about 30 cm. The LED panel Tx is connected to an arbitrary waveform generator (AWG) to produce an optical four-level pulse-amplitude-modulation (PAM4) signal. The LED panel was 58 cm \(\times\) 88 cm in size, and was obtained from Li-Cheng Corp.(r) (Taiwan, China). It consists of 108 white-light LED chips, each operating at 0.2 W. As a result, its total power consumption is 21.6 W. The authors of [34] also discussed other optical modulation techniques in turbulent aquatic environments. The distance between the LED panel and the mobile phone was 100 and 180 cm. A wave generator (Jeba(r) SCP-120) was used to produce water turbulence on the surface. The wave generator can only produce a single frequency of 2.8 Hz and a water ripple amplitude of 17%. As illustrated in [23], the water ripple amplitude percentage is defined as \(\left(\frac{h_{peak}-h_{\text{rev}}}{h_{peak}}\right)\times 100\%\), where \(h_{peak}\) is the peak water height and \(h_{\text{rev}}\) is the average water level. Figure 2b shows a photograph of the experiment: the LED panel Tx is mounted on a metal rack, and below is the water tank with the mobile phone and wave generator. During RS operation, the CMOS image sensor captures the optical signal row-by-row [29; 30]. As shown in Figure 3, if the LED Tx is modulated higher than the frame rate, bright and dark fringes representing Tx \"ON\" and \"OFF\" can be observed in the received RS pattern. RS operation allows the OCC data rate (>kbit/s) to be significantly higher than the frame rate (30/60 fps). Although RS operation can significantly enhance the data rate, decoding the data of the RS pattern may create ambiguity. During RS operation, the optical exposure of the subsequent pixel row will start before the exposure completion of the previous pixel row; hence, when the LED Tx is modulated at a high data rate, ambiguity occurs due to the uneven integration of light in the pixel row, as illustrated on the right-hand side of Figure 3. We can observe that the brightest fringe represents logic 1, and the darkest fringe represents logic 0. However, in some fringes, since the LED Tx \"ON\" state occurs during part of the exposure time, a gray fringe will result, making it unclear  
Figure 2: (**a**) Experimental setup of the wide FOV air-to-water OCC system using CuDNNLSTM with LED panel Tx and mobile phone CMOS camera for underwater Rx; AWG: arbitrary waveform generator. (**b**) Photograph of the experiment. whether it is logic value 1 or 0. This issue will be even more crucial when a PAM4 signal is transmitted. In this work, we employ CuDNNLSTM to enhance PAM4 OCC decoding. RS decoding is divided into training and testing stages using different received image frames. The training and testing frames number 50 and 318, respectively. A training data set of 50 frames is used for each BER measurement point. Hence, in each BER curve reported later, 500 frames will be used for the training data set. In this data set, 80% is used for training and 20% for validation. Each image frame will be processed in the image and data process module as shown in Figure 4. First of all, grayscale conversion is performed on each image frame so that each pixel has a value between 0 and 255, irrespective of color.