## 4 Results and Discussion  
Figure 10 shows the BER measurements of the RS-based UWOC system with PMMA SGOF using the proposed LSTM-NN model and PPB-NN model in the underwater and without water (i.e., air) cases. It can be observed that the BER curves with the water case and without the water case are similar at low data rates < 1.8 kbit/s. Without the water case, all of the BER curves can satisfy the pre-FEC threshold (i.e., BER \(\leq\) 3.8 \(\times\) 10\({}^{-3}\)), and the data rate can achieve 3 kbit/s. In the underwater case, we can observe that the water significantly affects the OCC signal detection by the image sensor; hence, choosing a suitable model is important. When PPB-NN is used, the data rate > 2.1 kbit/s cannot satisfy the pre-FEC threshold. However, when the LSTM-NN is used, a data rate of 2.7 kbit/s can be achieved, satisfying the FEC threshold. In the experiment, tap water is used, and it is at room temperature. As shown in Figure 2b, the water is stored in a water tank, and the experiment is performed at the laboratory in the afternoon. We believe that as long as the water density variation will not distort the rolling shutter pattern, UWOC performance will be unaffected. In this experiment, because we need to handle the problem of signal classification, we use the sparse categorical cross-entropy function as the loss function. We can use the loss function based on the training data and obtain the corresponding loss value in each epoch. The smaller the loss value, the better the model prediction. Here, for every 300 image frames, the first 30 image frames are used for training, and the rest of the 270 image frames are used for testing. Of the thirty training image frames, six of them will be used for validation; hence, the validation rate is 0.2. As shown in Figure 11, when the number of training times increases, the loss value decreases and starts to converge after 75 epochs. Because the loss value decreases as the number of training times increases, and it completely converges after 200 epochs with a loss value of <10\({}^{-3}\), the proposed LSTM-NN model has shown good accuracy. The learning rate, epoch, and batch size are 0.01, 200, and 30, respectively. Figure 10: BER measurements of the RS-based UWOC system with PMMA SGOF using the proposed LSTM-NN model and the PPB-NN model in the underwater and no water (i.e., air) cases. Here, we also investigate the error occurring using the PPB-NN model and the LSTM-NN mode, as illustrated in Figure 12a,b, respectively. The received data pattern and the predicted data pattern are also illustrated. At about the 250 th pixel in the received signal, there are several higher-intensity peaks. When applying the PPB-NN model, the predicted result is incorrect, as illustrated in Figure 12a. When applying the LSTM-NN model, the correct result can be observed, showing that the LSTM-NN model has better accuracy than the PPB-NN in underwater conditions. Figure 11: Loss value against epochs during LSTM-NN training. Figure 12: The predicted values when using the (**a**) PPB-NN model and (**b**) LSTM-NN model. Red arrow indicates the about 250 th pixel. As the PMMA SGOF is thin and only occupies a few pixels in the RS scanning direction, we also study the applicability of performing spatial multiplexing for enhancing the transmission capacity. Here, two fibers are put side by side at different separation distances, and we study the crosstalk produced. Due to the limited AWG and SGOF available in the laboratory, we can only experimentally evaluate two fibers carrying different random OOK data. Figure 13 shows the BER measurements at different data rates and different fiber separations of 0.5 cm, 3 cm, and 5 cm, respectively. The decoding algorithm is based on the LSTM-NN model described in Section 3. In order to get rid of the fiber reflection by the water tank, they are studied in the air condition. We can observe that the crosstalk is negligible when the fiber separation is at 5 cm. By decreasing the fiber separation, the crosstalk between the two fibers increases.