## 3 Design of Proposed Intelligent FSO System
### Transmitter Module
All simulations including the training and evaluation of the CNN, LeNet, and Wide ResNet models are conducted with a Windows 11 Home (64-bit) computer. The system was equipped with a 12 th Generation Intel(r) Core(tm) i9-12900H processor (20 threads, base frequency \(\sim\)2.5 GHz) and 32 GB of RAM. The results are divided into four parts as follows. ### Performance of FSO System Without Any Deep Learning Algorithms

Figure 5 illustrates the system performance over varying FSO link ranges under different atmospheric conditions, with subfigures (a) and (b) showing the Q-factor and BER, respectively. The analysis considers a minimum transmission distance of 0.6 km, with performance thresholds defined as a Q-factor of 4.07 and a BER of \(10^{-4}\). It is clear that, in the absence of power adaptation, the received signal quality is insufficient under severe weather conditions such as heavy fog and dry snow. Specifically, at a free space distance of 0.6 km, the system exhibits a BER of 1 and a Q-factor of 0, underscoring its inability to maintain reliable communication under such adverse scenarios. Given that dry snow imposes the most severe degradation among the evaluated weather conditions, the following analyses are restricted to this scenario. Figure 6 shows the influence of optical amplifier gain on BER performance under dry snow conditions for an FSO link length of 0.6 km. Due to the substantial attenuation of 96.8 dB/km introduced by dry snow, achieving reliable signal detection necessitates considerable optical amplification. One can observe from Figure 6 that when the total gain remains below 33.3 dB, the system fails to meet the BER target, exhibiting BER = 1. In

Figure 5: System performance over varying FSO link ranges under different atmospheric conditions. (a) Q-factor and (b) BER. contrast, increasing the gain leads to a marked enhancement in BER performance, with an acceptable threshold attained at a gain level of 35 dB. This observation illustrates how amplifier gain is applied incrementally based on the severity of signal degradation, as inferred from the predicted BER or Q-factor. For example, using the full 25 dB from the pre-channel amplifier with only 1 dB from the post-channel amplifier does not suffice to meet the BER threshold. As additional gain is introduced from the second amplifier, the BER improves progressively. Full gain from both amplifiers (25 dB + 10 dB) is only applied when necessary to achieve acceptable signal quality. This approach avoids unnecessary power usage and ensures efficient and adaptive amplification in response to real-time channel conditions. ### Performance Evaluation of CNN-Based BER Classifier

In this subsection, the results of using the CNN for BER classification will be discussed. Here, we used the first dataset. The model assigns a binary label to each input image, where a label of '1' indicates that the corresponding BER is below the predefined threshold, and a label of '0' denotes that it exceeds the threshold. Figure 7 shows an accuracy plot for the proposed CNN classifier. One can notice that the model demonstrates robust generalization, as the accuracy curve rapidly converges within the early epochs and remains consistently high throughout the training phase. Figure 6: BER versus overall gain of optical amplifiers. Figure 7: Accuracy curve for BER CNN classifier model. In the domain of computer vision, model evaluation is commonly conducted through a set of standardized performance indicators, including accuracy, precision, recall, and the F1-score. The corresponding mathematical expressions for these parameters are as following [26]:

\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}, \tag{2}\]

where T, P, F, and N, denote true, positive, false, and negative, respectively.