## 1 Introduction
### Related Work  
There are several studies that have used a CNN algorithm in optical communications. In [13], a CNN-based decoding method was proposed for differential FSO systems using bipolar complementary pulse width modulation (BCPWM), which improves signal amplitude and reduces common-mode interference. The model operates without channel state information by directly analyzing the received signals. The results showed that the CNN outperforms BNRZ, ML, and Volterra detectors, achieving lower SNR requirements for a target BER of \(10^{-5}\). The study focused solely on symbol-level signal demodulation using CNNs and required raw received signal waveforms as input. This approach does not address practical scenarios where direct signal access is limited or where only eye diagrams are available for performance monitoring. Additionally, the work lacks an investigation related to estimating key physical-layer performance metrics such as the BER and Q-factor, which are crucial for real-time optical system assessment and control. These gaps highlight the need for more adaptable CNN-based models that can extract meaningful performance insights from visual signal representations like eye diagrams without relying on prior channel knowledge. In [14], a custom CNN model was designed and experimentally validated to classify On-Off Keying (OOK) bit sequences under varying Signal-to-Noise Ratios (SNRs), simulating thermal noise and atmospheric turbulence conditions. The study employs two CNNs: the first detects whether a given signal segment contains bit information, while the second classifies detected bits as logical '1' or '0'. This two-stage approach helped reduce computational complexity during demodulation. The study demonstrated the effectiveness of CNNs in supporting high-throughput optical communication in noisy environments, offering reliable bit classification without imposing significant constraints on the size, weight, and power of the FSO setup. However, a key limitation of this approach is its reliance on controlled experimental conditions, which may not fully capture real-world turbulence dynamics. Additionally, the need for training separate networks adds system complexity and may introduce latency in real-time applications, especially under rapidly varying channel conditions. The study in [15] used an end-to-end deep learning-based FSO communication system employing OOK modulation, demonstrating its ability to significantly reduce the BER without relying on channel state information (CSI). While effective, such systems often face challenges when dealing with dynamic or unpredictable channel variations that can degrade when the inference conditions deviate from the training scenarios. In contrast, the approach adopted in this study focuses on using CNNs, LeNet, and Wide ResNet to directly evaluate the BER and Q-factor from eye diagram images, which are widely recognizedas visual indicators of signal integrity. This approach not only circumvents the need for explicit CSI but also leverages standard physical-layer diagnostic tools in optical networks. Furthermore, using eye diagrams as input allows for more generalized feature extraction, enabling robust performance monitoring across varying conditions without retraining for each specific channel model. The work in [16] introduced a Deep Transfer Learning (TL) framework for a comprehensive analysis and diagnosis of eye diagrams in Optical Fiber Communication (OFC) systems. It employed a CNN algorithm to estimate fiber link lengths and extract essential eye diagram parameters such as the Q-factor, eye height, and eye width. By reusing learned features from a source task (e.g., estimating link length) for multiple target tasks (e.g., parameter estimation and impairment classification), the method achieved high accuracy and significantly reduced training times. While the results were good, the scope of the study was primarily limited to impairments caused by device imperfections and signal degradation in guided media.