학술논문

Performance Evaluation of Optical Transmission Based on Link Estimation by Using Deep Learning Techniques
Document Type
Periodical
Source
IEEE Access Access, IEEE. 12:64126-64139 2024
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Optical fiber networks
Optical fiber communication
Optical fibers
Optical imaging
Fiber optics
Adaptive optics
Optical pulses
Artificial neural networks
Convolutional neural networks
Q-factor
Deep neural network
convolutional neural networks
pulse amplitude modulation
eye diagram
non-return-to-zero
intensity modulation-direct detection
Language
ISSN
2169-3536
Abstract
In optical communication systems, the Q-factor is an important performance metric to evaluate the performance of an optical link. In this paper, a deep learning-based eye diagram analyzer is proposed to estimate the Q-factor. CNN architectures such as LeNet, Wide ResNet, and Inception-v4 are used for ON-Off Keying (OOK) and Pulse Amplitude Modulation (PAM) formats’ eye diagrams. The performance of these architectures is evaluated in terms of accuracy, Mean Squared Error (MSE), and error tolerance. This work shows that Wide ResNet demonstrates better performance in both OOK and PAM4 transmission schemes, achieving MSE values of 0.00188 and 0.00036, respectively. Additionally, it attains a high R-squared (R2) value of 0.9998. This deep learning-based eye diagram analyzer may be a promising approach for analyzing and optimizing optical communication systems without extensive human intervention.