Next-Generation Adaptive Nonlinear Equalization Algorithm Using Machine Learning for High-Speed Optical Communication
A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Optical Interaction Science".
Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 1850
Special Issue Editors
Interests: optical communication; specifically in areas such as fiber transmission; transmitter optimization; nonlinear equalizers; signal processing
Special Issue Information
Dear Colleagues,
High-spend data exchange between optical fiber communication systems is critical to support various ever-emerging Internet applications, including virtual reality, online shopping, and other services. Stimulated by exponential traffic growth, high-speed, large-capacity transmission systems have attracted increasing research interest worldwide. However, various impairments arising from both the transceivers and transmission link degrade the transmission performance and need to be mitigated for the purpose of maximizing the transmission capacity. Besides the traditional model-assisted equalization, machine learning based equalization schemes are considered promising in order to compensate for the transmission distortions because of their superior performance, as seen with the multilayer perceptron, long short-term memory neural network, and convolutional neural network.
The current Special Issue aims to report the scientific research that not only includes a nonlinear equalization method directly via machine learning or a machine learning-assisted nonlinear equalization method at the receiver-side, but also includes a transmitter-side pre-equalization method using machine learning. Above all, we encourage submissions that cover the integration of optical communication and machine learning.
Dr. Meng Xiang
Dr. Xueyang Li
Guest Editors
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