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

Waddah S. Saif, Sunish Kumar Orappanpara Soman, Octavia A. Dobre

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
45 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)8162-8172
Number of pages11
JournalJournal of Lightwave Technology
Volume42
Issue number23
Early online date12 Jul 2024
DOIs
Publication statusPublished (in print/issue) - 1 Dec 2024

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Complexity theory
  • Mathematical models
  • Optical fiber networks
  • Optical fiber polarization
  • Symbols
  • Optical fiber filters
  • Q-factor
  • Coherent communications
  • machine learning
  • nonlinearity compensation
  • optical fiber communications
  • subcarrier multiplexing

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