## 3 Design of Proposed Intelligent FSO System
### Transmitter Module
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Task** & **Metric** & **BER** & **Q-factor** \\ \hline CNN (BER) & Classification & Accuracy & 99\% & – \\ \hline \multirow{2}{*}{LeNet (BER)} & \multirow{2}{*}{Regression} & MSE & 0.0049 & \multirow{2}{*}{–} \\ \cline{3-3} \cline{5-5}  & & R\({}^{2}\) score & 0.40 & \\ \hline \multirow{2}{*}{LeNet (Q-factor)} & \multirow{2}{*}{Regression} & MSE & – & 0.0757 \\ \cline{3-3} \cline{5-5}  & & R\({}^{2}\) score & & 0.9688 \\ \hline \multirow{2}{*}{Wide ResNet (Q-factor)} & \multirow{2}{*}{Regression} & MSE & \multirow{2}{*}{–} & 0.0555 \\ \cline{3-3} \cline{5-5}  & & R\({}^{2}\) score & & 0.9787 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance summary of various DL models. Figure 14: Predicted versus true values of the Q-factor using the Wide ResNet model. (**a**) Actual and (**b**) predicted. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Value** & **Description** \\ \hline Total parameters & 320,385 & Number of trainable and non-trainable weights in model \\ \hline Trainable parameters & 319,457 & Parameters updated during training \\ \hline Non-trainable parameters & 928 & Parameters fixed during training (e.g., BatchNorm stats) \\ \hline Model size & \(\sim\)1.22 MB & Disk size of model weights \\ \hline Total FLOPs & \(2,475,527,265\) (\(\approx\)2.48 GFLOPs) & Floating Point Operations (FLOPs) per forward pass \\ \hline Average inference time & 82.63 ms/image & Measured over 100 runs using a single sample \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Wide ResNet model complexity and inference time. equipped with a 12 th Gen Intel(r) Core(tm) i9-12900H processor and 32 GB RAM, using a CPU-only setup in a Jupyter Notebook version 7.3.2 environment. Table 5 summarizes the key complexity metrics. Although our proposed custom CNN BER classifier model presents a higher parameter count (5.42 million), larger memory footprint (20.70 MB), and longer inference time (0.1958 s) in comparison with benchmark models such as LeNet and Wide ResNet as observed in Table 5, it is important to note that these values were obtained under a CPU-only evaluation setup. In practical deployment scenarios, significant reductions in inference latency can be achieved through GPU acceleration or model optimization strategies such as quantization and pruning. Furthermore, the complexity of the proposed CNN is justified by the nature of its task: classifying whether the BER lies above or below a mission-critical threshold under noisy FSO conditions. This classification task necessitates finer feature extraction and the use of higher-resolution inputs (128 \(\times\) 96), unlike the regression models that operate on coarser inputs (64 \(\times\) 64) and predict a continuous BER value. Hence, the proposed model offers a necessary balance between accuracy and computational cost for reliable BER threshold detection in challenging optical environments. Figure 15 illustrates the comparison of total parameters, memory footprint, and inference latency for the studied models. The LeNet architecture achieved a good balance between performance and efficiency, with relatively small parameter sizes and the lowest inference times. The Wide ResNet model demonstrated similar computational efficiency while maintaining model compactness due to its residual structure. The custom CNN, designed for classification, involved significantly more parameters and computation due to higher input resolution and deeper dense layers, making it more suitable for high-performance systems.