Optimization algorithms of neural networks for traditional time-domain equalizer in optical communications

Haide Wang, Ji Zhou, Yizhao Wang, Jinlong Wei, Weiping Liu, Changyuan Yu, Zhaohui Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


Neural networks (NNs) have been successfully applied to channel equalization for optical communications. In optical fiber communications, the linear equalizer and the nonlinear equalizer with traditional structures might be more appropriate than NNs for performing real-time digital signal processing, owing to its much lower computational complexity. However, the optimization algorithms of NNs are useful in many optimization problems. In this paper, we propose and evaluate the tap estimation schemes for the equalizer with traditional structures in optical fiber communications using the optimization algorithms commonly used in the NNs. The experimental results show that adaptive moment estimation algorithm and batch gradient descent method perform well in the tap estimation of equalizer. In conclusion, the optimization algorithms of NNs are useful in the tap estimation of equalizer with traditional structures in optical communications.

Original languageEnglish
Article number3907
JournalApplied Sciences (Switzerland)
Issue number18
Publication statusPublished - 18 Sep 2019


  • Equalizer
  • Neural networks
  • Optical communications
  • Optimization
  • Tap estimation

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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