## 3 Design of Proposed Intelligent FSO System
### Transmitter Module
Around epoch 40, both training and validation losses stabilize, suggesting convergence. The small gap between training and validation curves, coupled with a final validation MSE of 0.0757 (Q-factor) and 0.00049 (BER), reflects strong generalization. Moreover, the model achieves an R\({}^{2}\) score of 0.9688 for Q-factor prediction and 0.40 for BER prediction, underscoring its effectiveness and low risk of overfitting in practical FSO systems. Figure 9: The confidence visualization of the BER CNN classifier for a sample eye diagram image. Figure 11 presents a comparison between the predicted and actual values for both the Q-factor and the BER. In Figure 11, the predicted Q-factor values align closely with the diagonal reference line, indicating accurate regression performance. Similarly, Figure 11 shows strong agreement between the predicted and true BER values. These results confirm that the LeNet-based model can effectively approximate both performance metrics from eye diagram inputs. Table 2 illustrates a comparison between the predicted and actual Q-factor and BER values for eye diagram samples extracted from a different dataset acquired under amplification at a maximum transmission distance of 0.6 km under dry snow. This evaluation is intended to assess the generalization performance of the LeNet model under previously unseen conditions. The predicted Q-factor of 4.234272 demonstrates a minimal percentage error of approximately 0.101% relative to the true value of 4.230000, indicating highly precise regression performance. Similarly, for the BER, the predicted value of \(1.116\times 10^{-5}\) deviates from the true value of \(1.2758\times 10^{-5}\), yielding a percentage error of approximately 12.53%. These results confirm that the LeNet-based model maintains strong predictive accuracy for both performance metrics, even when applied to input conditions not encountered during training. Figure 5:0 Predicted versus true values using the LeNet model. (**a**) The Q-factor and (**b**) BER. Figure 5:1 MSE loss versus epoch for (**a**) Q-factor and (**b**) BER. Finally, the system utilizes the predicted BER or Q-factor to make an informed decision regarding data retransmission with amplification. If the predicted BER exceeds a predefined threshold or the Q-factor falls below an acceptable level, this implies that the received signal quality is inadequate. Such degradation may arise from insufficient transmitter power, misalignment, or environmental impairments. In this case, optical amplification is employed to enhance signal reliability. Conversely, if the predicted BER is within acceptable limits and the Q-factor is sufficiently high, the data are considered successfully received and are forwarded to the end users without requiring retransmission. ### Performance Evaluation of Wide ResNet-Based Model for Q-Factor Prediction

To evaluate the performance of the Wide ResNet architecture in regressing the Q-factor from eye diagram images, dataset 2 comprising 5000 frames is employed. This dataset is selected because Wide ResNet models typically require larger training sets than shallower architectures such as LeNet, due to their increased capacity and deeper hierarchical structure. The model is trained for up to 100 epochs with early stopping enabled, and convergence is achieved at epoch 49. The larger model capacity of Wide ResNet allows it to better extract abstract spatiotemporal features from complex eye diagrams, making it well suited for Q-factor prediction under varying transmission conditions. However, when applying the Wide ResNet architecture to predict BER values, the model did not generalize effectively. One possible reason for this limited performance is the small dynamic range and sparsity of BER values (ranges from \(10^{-20}\) to \(10^{-3}\)), which can make it challenging for deep architectures to learn stable regression mappings without the use of specialized loss functions or value scaling strategies.