End-to-end optimization based on residual neural networks for improved optical fiber communication
Alnaseri O. Ala’anzy M.A. Saeed N.
2025Korean Institute of Communications and Information Sciences
ICT Express
2025
High-capacity optical fiber transmissions increasingly face performance limits imposed by nonlinear transmission effects. This paper examines deep learning-based enhancements in optical fiber communication through an end-to-end autoencoder (AE) framework. A residual neural network (ResNet) architecture is employed to replace traditional dense layers, which uses shortcut connections for improved learning under nonlinear channel impairments. Using the achievable information rate (AIR) as the performance metric, the ResNet-based AE demonstrates 2.6% superior compared to dense-layer AE under high nonlinearity conditions. Although dense layer AEs offer lower complexity, the ResNet achieves 3.75% Q-factor improvements, making it a compelling choice for next-generation high-speed optical systems.
Achievable information rate , Bit-wise autoencoder , End-to-end deep learning , Optical fiber communication , Residual neural network
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Department of Electrical Engineering, Baden-Wuerttemberg Cooperative State University Ravensburg, Campus Friedrichshafen, Germany
Department of Computer Science, SDU University, Kaskelen, Kazakhstan
Department of Electrical and Communication Engineering, United Arab Emirates University (UAEU), United Arab Emirates
Department of Electrical Engineering
Department of Computer Science
Department of Electrical and Communication Engineering
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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026