## 2 Related Studies--A Brief Review
The parameters are modified to reduce discrepancies between the desired and ANN outputs [38]. Improving the ANN on the receiver can bring several benefits, such as reducing training time and complexity, maintaining high performance, achieving high data rates and bandwidth transmission capabilities, improving efficiency, and enhancing multipath delay robustness. Various studies have highlighted these advantages, including references [39; 40; 41; 31; 12; 30; 42; 31]. A 10 Gbps NRZ modulation scheme measures the SNR using statistical parameters such as means and standard deviations obtained from the ADTP. The RMSE of the ADTP is 0.73. In [31; 44; 45; 46; 47; 48; 49], a DNN was employed to classify SNR from AAH and 16-QAM PDM-64 QAM with an accuracy that can reach 100%. OSNR monitoring from 10 to 30 dB was achieved using 10 Gb/s NRZ-OOK and NRZ-DPSK from ASCS. Constellation diagrams were used in [50; 51] to estimate SNR with errors less than 0.7 dB by designing CNNs with QPSK, PSK, and QAM modulation formats. The fundamental CNN algorithm for SNR estimation is presented in [52], and methods for preprocessing received signals and selecting optimal parameters are provided. The technique efficiently and accurately identifies the modulation format and estimates SNR and BER using 3D constellation density matrices in Stokes space. Eye diagrams have been utilised in the literature to track OSNR, PMD, CD, nonlinearity, and crosstalk via NNs [53; 54; 55]. Observing an eye pattern involves various optical communication noises simultaneously (e.g., thermal noise, time jitter, and ISI); consequently, SNR decreases, and the signal declines when the noise levels rise. SNR is used to investigate the quality of the received signal in communication systems. Reference [56] presents a long short-term memory (LSTM)--based deep learning to simultaneously estimate SNR and nonlinear noise power. The test error is less than 1.0 dB, and the modulation types include QPSK, 16 QAM, and 64 QAM. The SNR monitoring method suggested in ref. [57] uses an LSTM neural network, a classifier, and a low-bandwidth coherent receiver to convert continuous monitoring into a classification problem. It is cost-effective and suitable with multi-purpose OPM systems because it achieves excellent classification accuracy and robustness with minimal processing complexity. The eye diagram used to locate optical signal impairments by overlapping the symbols depicts the amplitude distribution over one or more-bit periods. SNR and BER indicate how well a system performs by assessing the signal quality based on various properties: eye height, eye width, jitter, cross percentage, and levels 0 and 1 (Figure 2). SVM for classification and NN for regression were studied in ref. [25; 26; 27; 38] using 64-QAM, 40 Gb/s RZ-OOK, 10 Gb/s NRZ-OOK, and DPSK. The input features from the eye diagram are mean, variance, Q-factor, closure, jitter, and crossing amplitude. The ANN reports a correlation coefficient of 0.97 and 0.96 for OOK and DPSK systems, respectively [58]. Figure 2: Eye diagram essential features. NN regression was developed to extract variance from eye diagram images, and SNR with a range from 4 to 30 dB was measured with a mean estimation error range of 0.2 to 1.2 dB for 250 km [25]. Another study used an ANN to extract 24 features from eye diagram images. The RSME values ranged from 1.5 to 2 for SNRs between 10 and 30 dB using NRZ, RZ, and QPSK for a data rate of 40 Gb/s [59]. Table A1 (in Appendix A) represents the studies from 2009 to 2024 that used ML to extract features from eye diagram images to obtain signal-to-noise ratios; the table also shows the implementations of the NN algorithms and model performance and compares these studies with ours. References [24; 60; 61] demonstrated the CNN-based algorithms on eye diagram images and discussed the CNN structure and implementations in detail. These studies have generated eye diagrams by run-time simulation or experimental setup and used classification techniques to obtain the SNR (see Table A2 in Appendix A).