## 1 Introduction  
Over the past several decades, mobile communication systems have undergone a rapid evolution, with each generation emerging roughly every ten years since the introduction of first-generation (IG) networks in the 1980s. The allocated carrier frequency spectrum of 1G to fourth-generation (4G) technologies has primarily ranged from 700 MHz to 2.6 GHz [1]. However, the limited bandwidth within this range, coupled with the exponential increase in mobile device penetration and global data traffic, has resulted in persistent challengesrelated to spectrum congestion and capacity limitations. These issues have been further exacerbated by the advent of data-intensive applications enabled by emerging technologies such as fifth- and sixth-generation (5G and 6G) networks, the Internet of Things (IoT), smart urban infrastructure, and cloud-based platforms. The growing demand for high-throughput, low-latency, and reliable communication services has placed a significant strain on existing Radio Frequency (RF) resources, which are increasingly constrained by spectral scarcity and interference. In response to these challenges, FSO communication has attracted considerable interest as a promising complementary solution. FSO systems offer several key advantages, including fiber-comparable data rates, rapid and cost-effective deployment, unlicensed access to a wide optical frequency band, and enhanced physical layer security [2; 3]. FSO communication has demonstrated versatility across a wide range of applications, including inter-building links, healthcare services, defense systems, and various other domains [4]. Despite its promising capabilities, FSO technology remains highly sensitive to environmental factors. Specifically, its performance can be significantly affected by atmospheric disturbances and adverse weather conditions such as scintillation, fog, haze, rainfall, and snow, as well as misalignment-induced pointing errors between the optical transmitter and receiver [5]. These impairments contribute to signal attenuation and result in transmission power losses that can severely degrade the reliability and overall efficiency of the FSO communication link. FSO systems use narrow beams within the Near-Infrared (NIR) range to establish Line-of-Sight (LoS) communication. The 1550 nm wavelength is commonly preferred for its low atmospheric attenuation and superior receiver sensitivity [5; 6]. While FSO systems inherently provide high data rates and low latency owing to their direct LoS transmission, the increasing complexity of optical network infrastructures presents challenges in accurately assessing signal quality [7]. These challenges stem from dynamic network configurations and varying environmental conditions that affect link performance. Recent advances in optical signal measurement and analysis have introduced effective tools for comprehensive performance monitoring, facilitating the more reliable management of optical links across diverse operational scenarios [8]. The eye diagram serves as a fundamental tool for evaluating signal quality within optical communication systems. It encapsulates essential information about the optical signal's behavior alongside other system-related parameters. Notably, visual data such as eye diagrams offer meaningful insights into the condition and performance of the signal. Despite being static in nature, these eye diagram visualizations effectively illustrate the system's response under defined operating conditions. They have been widely utilized to assess signal integrity and to characterize various impairments commonly encountered in Intensity Modulation-Direct Detection (IM-DD)-based optical systems [9]. Traditional methods for analyzing eye diagrams typically depend on expert interpretation, which can introduce human error and increase operational overhead. To mitigate these limitations, machine learning (ML) algorithms have been increasingly adopted in optical communication systems for automated signal quality assessment [10].