TY - JOUR

T1 - Compensation of nonlinear signal distortions in optical fiber communication systems

AU - Redyuk, Alexey

AU - Sidelnikov, Oleg

AU - Fedoruk, Mikhail

N1 - The work was supported by the Russian Science Foundation, Russia (Project No. 20-11-20040). The work of A.R. was supported by the grant for the implementation of the strategic academic leadership program “Priority 2030” in Novosibirsk State University.

PY - 2025/4

Y1 - 2025/4

N2 - 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.

AB - 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.

KW - Fiber nonlinearity

KW - Nonlinear signal distortions

KW - Nonlinearity compensation

KW - Optical communications

UR - https://www.mendeley.com/catalogue/c638eb2f-82b4-379d-826f-71135710bddc/

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85214303126&origin=inward&txGid=9e6a0c266723b309fe505f446d135af2

U2 - 10.1016/j.optcom.2024.131418

DO - 10.1016/j.optcom.2024.131418

M3 - Article

VL - 578

JO - Optics Communications

JF - Optics Communications

SN - 0030-4018

M1 - 131418

ER -