## 3 Design of Proposed Intelligent FSO System
### Transmitter Module
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **LeNet (BER)** & \begin{tabular}{c} **LeNet** \\ **(Q-factor)** \\ \end{tabular} & \begin{tabular}{c} **Wide ResNet** \\ **(Q-factor)** \\ \end{tabular} & **CNN (BER)** \\ \hline Purpose & Regression & Regression & Regression & Classification \\ \hline Total Parameters & 337,421 & 337,421 & 320,385 & 5,425,793 \\ \hline \begin{tabular}{c} Trainable \\ Parameters \\ \end{tabular} & 337,421 & 337,421 & 319,457 & 5,425,793 \\ \hline \begin{tabular}{c} Non-Trainable \\ Parameters \\ \end{tabular} & 0 & 0 & 928 & 0 \\ \hline \begin{tabular}{c} Model Size (MB) \\ \end{tabular} & \(\sim\)1.29 & \(\sim\)1.29 & \(\sim\)1.22 & \(\sim\)20.70 \\ \hline \begin{tabular}{c} Average Inference \\ Time (s) \\ \end{tabular} & 0.1450 & 0.1402 & 0.08263 & 0.1958 \\ \hline \
\begin{tabular}{c} Input Image Size \\ \end{tabular} & 64 \(\times\) 64 & 64 \(\times\) 64 & 64 \(\times\) 64 & 128 \(\times\) 96 \\ \hline
\begin{tabular}{c} Dataset \\ \end{tabular} & 1000 & 1000 & 5000 & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between computation complexity for various DL models. ## 5 Conclusions

This work introduced a set of CNN models namely, LeNet, Wide ResNet, and a lightweight CNN classifier, for the intelligent assessment of signal quality for FSO systems. These models are used to determine whether the received signal meets predefined quality thresholds by predicting the BER or Q-factor, or by directly classifying the BER (from received eye diagrams), thereby enabling adaptive retransmission with power amplification strategies. For the regression tasks, LeNet and Wide ResNet are used to predict BER and Q-factor values from eye diagram images. Their performance is evaluated using MSE and the R\({}^{2}\) score. LeNet, with \(\sim\)337k trainable parameters, achieved inference times of 0.1450 s and 0.1402 s per image for BER and Q-factor prediction, respectively. Wide ResNet, with \(\sim\)320k parameters and a complexity of \(\sim\)2.48 GFLOPs, achieved comparable accuracy with a slightly faster inference time of 82.63 ms/image. In the classification task, a CNN model is trained to categorize signal quality into \"acceptable\" or \"unacceptable\" based on a BER threshold. Model performance is evaluated using accuracy, confirming reliable decision-making capabilities under different channel conditions, and it achieves an accuracy of 99%. To validate model generalizability, we tested all models on unseen eye diagram samples from a different dataset. The models successfully maintained strong performance across these external samples, demonstrating their robustness and practical applicability. Overall, the proposed approach provides a learning-based solution for real-time signal quality estimation. These models can be integrated into adaptive transmission frameworks to enhance the reliability of FSO systems. Although the dataset used in this work was collected under weather-induced attenuation conditions, the proposed CNN-based models are applicable to any eye diagrams that reflect signal impairments whether due to atmospheric turbulence, attenuation, or pointing errors. Since eye diagrams encapsulate cumulative signal degradation, future work will explore the application of the proposed system to datasets generated under Gamma-Gamma and Log-Normal fading models, as well as channels with pointing error effects. While this study focuses on the BER and Q-factor as primary performance indicators, future work will extend the proposed framework to address adaptive throughput management to compensate for wavefront distortion, where real-time signal quality predictions can guide the selection of data rates or modulation schemes in FSO systems.