Deep Learning-Assisted Nonlinearity Compensation in Subcarrier-Multiplexing Coherent Optical Systems

• Published in: Journal of lightwave technology pp. 1 - 11
• Main Authors: Saif, Waddah S., Soman, Sunish Kumar Orappanpara, Dobre, Octavia A.
• Format: Journal Article
• Language: English
• Published: IEEE 11.07.2024
• Subjects: Coherent communications; Complexity theory; machine learning; Mathematical models; nonlinearity compensation; optical fiber communications; Optical fiber filters; Optical fiber networks; Optical fiber polarization; Q-factor; subcarrier multiplexing; Symbols
• ISSN: 0733-8724; 1558-2213
• DOI: 10.1109/JLT.2024.3427121

Fiber nonlinearity imposes limitations on the transmission distances in optical fiber networks. Fiber nonlinearity compensation (NLC) becomes essential for extending the transmission reach; however, conventional methods like digital backpropagation (DBP) experience challenges related to the intricacies of computational demands. To mitigate the fiber nonlinearity cost-effectively, subcarrier multiplexing (SCM) emerges as a promising solution compared to single-carrier systems. However, the SCM performance is limited by nonlinear effects such as self-subcarrier nonlinearity (SSN) and cross-subcarrier nonlinearity (CSN). In previous studies, a combination of SCM with DBP, named SCM-DBP, has been employed to address these issues. Concurrently, deep learning-assisted NLC, for example, learned DBP (LDBP), has shown promise in enhancing performance and reducing complexity. In this paper, we aim to apply learning to the SCM-DBP by holistically combining the principles of the SCM-DBP and LDBP approaches, denoted as SCM-LDBP, to mitigate SSN and CSN cost-effectively. To investigate the efficacy of our proposed SCM-LDBP technique, we carry out numerical simulations for both a contemporary 32 Gbaud and a strategic 120 Gbaud SCM transmission system over a 1600km optical fiber link. With only two interfering subcarriers in the back-propagation routine, our proposed SCM-LDBP demonstrates a 0.3dB Q factor improvement and a 31.7% complexity reduction in the 32 Gbaud system when compared to the SCM-DBP. Similarly, in the 120 Gbaud system, the proposed SCM-LDBP demonstrates a 0.2dB Q factor improvement and a 37.8% reduction in complexity over the SCM-DBP technique.