## 1 Introduction
### Related Work
In contrast, the proposed study addresses eye diagram-based signal quality estimation in FSO communication systems, where the signal is susceptible to fundamentally different challenges such as atmospheric attenuation, atmospheric turbulence, beam misalignment, and weather-induced attenuation. These impairments require specialized learning models that can accommodate the stochastic and dynamic nature of FSO channels, representing a distinct research challenge not covered in OFC systems. Additionally, the paper in [17] introduced a deep learning-driven approach for Q-factor prediction in OFC systems, employing CNN architectures such as LeNet, Wide ResNet, and Inception-v4. These models were trained on eye diagrams corresponding to OOK and level four Pulse-Amplitude Modulation (PAM4 formats). Similarly, the study did not address the unpredictable-prone environment of FSO links. In contrast, our study targets eye diagram analysis in FSO systems, where deep learning models must contend with atmospheric effects and more volatile channel conditions, introducing a fundamentally different set of challenges. In [18], the authors tackled the complex issue of optimal power distribution for wavelength division multiplexing (WDM)-based radio over FSO (RoFSO) systems by introducing two adaptive algorithms guided by channel state information (CSI). Initially, they proposed a model-driven Stochastic Dual Gradient (SDG) approach that offers exact solutions but requires accurate system modeling and reliable CSI estimation. To address these dependencies, a second, model-independent method was developed, namely, the Primal-Dual Deep Learning (PDDL) algorithm. This approach employs deep neural networks to learn the optimal power allocation strategy by simultaneously updating primal and dual variables, without needing explicit knowledge of the system or channel model. A policy gradient technique is used to compute the learning updates. The simulation results demonstrated that both proposed techniques significantly outperform traditional equal power allocation schemes. The model-free PDDL method, in particular, shows strong potential for broader deployment in FSO systems, where modeling complexity and turbulence estimation remain major challenges. In [19], an embedded power allocation framework was proposed for optical wireless communication systems operating over various fading environments, including foggy conditions, Rayleigh and Rician fading, and the Gamma-Gamma turbulence model. This system facilitates real-time power optimization under noisy and fluctuating channel conditions, helping to enhance transmission speed and minimize signal interference. Notably, the solution demonstrated high accuracy levels reaching approximately 99% in Rician fading channels, 97% in standard and Rayleigh scenarios, and 96% in fog-affected environments. Additionally, it achieved maximum data rates of 2.8 Gbps for binary phase shift keying (BPSK), 5.6 Gbps for quadrature phase shift keying (QPSK), 11.2 Gbps for 16 level quadrature amplitude modulation (16 QAM), and 16.81 Gbps for 64 QAM. In [20], the problem of optical power allocation was investigated under both power budget and eye safety constraints for adaptive WDM-based FSO transmission systems. The study aimed to mitigate the impact of weather-induced turbulence on signal quality and system performance. For conventional systems, a basic water-filling algorithm was proposed to distribute power across multiple wavelengths. However, for WDM RoPSO systems, a dedicated power allocation algorithm was developed, demonstrating superior capacity and robustness. The results showed that this method could maintain high data rates even over long distances or under challenging weather conditions. Several studies have explored the application of deep learning models, particularly CNNs, for extracting critical features from eye diagrams to estimate key performance parameters such as the Q-factor and bit error rate in optical communication systems.