MACHINE LEARNING METHODS FOR COMPENSATING SIGNAL DISTORTIONS IN FIBER-OPTIC COMMUNICATION LINES
O. S. Sidelnikov, A. A. Redyuk, M. P. Fedoruk
Novosibirsk State University, Novosibirsk, Russia
Keywords: fiber optic communication systems, optical fiber nonlinearity, nonlinear distortion compensation, neural networks, machine learning, digital signal processing
Abstract
The article addresses current issues in the field of fiber-optic data transmission, related to the constant increase in demand for communication system bandwidth and nonlinear response. The main machine learning methods used to compensate for nonlinear signal distortions in long-haul coherent communication lines are presented, including neural networks of various architectures. The paper emphasizes the promising nature of machine learning-based solutions to enhance the performance of optical fiber communication systems, thanks to their ability to derive effective and adaptive signal recovery schemes with low computational complexity.
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