Compensation of nonlinear signal distortions in optical fiber communication systems

This paper examines the significant challenge of nonlinear signal distortions in long-haul optical fiber communication systems, which notably limit performance as data rates and transmission distances increase. We evaluate current approaches for compensating these distortions, emphasizing the efficiency of digital backward propagation and its enhanced versions, which leverage the physical laws of signal propagation. While digital backward propagation demonstrates superior performance, its high computational demands hinder widespread application in modern digital signal processing systems. Furthermore, we highlight the emerging role of machine learning techniques, particularly deep neural networks, in addressing nonlinearity by uncovering complex signal relationships and improving compensation efficiency. However, practical implementation remains constrained by hardware limitations and the complexities of training algorithms. This overview underscores the necessity for ongoing research to develop robust, efficient solutions that balance performance with computational feasibility in the pursuit of advanced optical communication technologies.