## 3 Design of Proposed Intelligent FSO System
### Transmitter Module
Therefore, we did not consider the Wide ResNet algorithm for BER analysis and it was restricted to the Q-factor. Interestingly, a simpler LeNet model trained on a smaller dataset of only 1000 eye diagram frames demonstrated better generalization in BER prediction. This suggests that lightweight architecture may be more effective in scenarios where data distributions are narrow or heavily skewed. Figure 12 demonstrates the training and validation MSE trajectories observed during the optimization of the Wide ResNet model on dataset 2. The early stages of training exhibit transient instability in validation error, which is characteristic of deeper models undergoing weight adaptation. This variability diminishes over time, and both curves gradually converge to low MSE values, indicating effective parameter learning and stable generalization. The minimal gap between training and validation losses in the later epochs reflects the benefit of architectural regularization strategies such as dropout and weight penalties and supports the model's suitability for inferring Q-factor from high-dimensional visual input. The eventual convergence at epoch 49 suggests that, given enough samples, deeper convolutional architectures can achieve consistent performance in regression tasks involving complex signal representations. Moreover, the model attains an R\({}^{2}\) score of 0.9787 and a final MSE of 0.0555 for Q-factor prediction, demonstrating its high predictive accuracy and minimal overfitting risk in practical FSO communication systems. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **True Value** & \
\begin{tabular}{c} **Predicted** \\ **Value** \\ \end{tabular} & **Absolute Error** &
\begin{tabular}{c} **Percentage** \\ **Error** \\ \end{tabular} \\ \hline Q-factor & 4.23 & 4.234272 & 0.004272 & 0.101\% \\ \hline BER & \(1.2758\times 10^{-5}\) & \(1.116\times 10^{-5}\) & \(1.598\times 10^{-6}\) & 12.53\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between true and predicted Q-factor and BER using LeNet-based regression model. Figure 13 displays the scatter plot of predicted versus actual Q-factor values on the validation set using the Wide ResNet model. The red dashed line represents the perfect prediction. The points are tightly clustered around this reference line across the full Q-factor range, indicating a strong linear correlation between predicted and ground-truth values. Additionally, to evaluate the generalization capability of the Wide ResNet model under previously unseen transmission conditions, a representative eye diagram image was selected from a separate dataset originally used for testing the LeNet model. As illustrated in Figure 14, the predicted and actual Q-factor values for this unseen sample exhibit a close match, with a relative error of approximately 0.95%. To evaluate the computational complexity and efficiency of the proposed Wide ResNet architecture, we measured the number of trainable parameters, floating-point operations (FLOPs), and average inference time per image, which are given in Table 3.

Figure 12: Training and validation MSE curves for Wide ResNet-based Q-factor regression model. Figure 13: Predicted versus true Q-factor values using the Wide ResNet model. Table 4 shows the summarized results obtained from the three CNN models that are used in this study. Classification accuracy and confusion breakdown are shown for the custom BER classifier. Regression models (LeNet and Wide ResNet) are evaluated using the MSE and R\({}^{2}\) score for Q-factor and BER prediction tasks. ### Computation Complexity

Each model is trained and tested using black-and-white-scale image inputs with varying resolutions. Inference time was measured on a Windows 11 Home (64-bit) machine