Parallelization of Recurrent Neural Network-Based Equalizer for Coherent Optical Systems via Knowledge Distillation

Sasipim Srivallapanondh, Pedro J. Freire, Bernhard Spinnler, Nelson Costa, Antonio Napoli, Sergei K. Turitsyn, Jaroslaw E. Prilepsky

Research output: Contribution to journalArticlepeer-review


The recurrent neural network (RNN)-based equalizers, especially the bidirectional long-short-term memory (biLSTM) structure, have already been proven to outperform the feed-forward NNs in nonlinear mitigation in coherent optical systems. However, the recurrent connections still prevent the computation from being fully parallelizable. To circumvent the non-parallelizability of recurrent-based equalizers, we propose, for the first time, knowledge distillation (KD) to recast the biLSTM into a parallelizable feed-forward 1D-convolutional NN structure. In this work, we applied KD to the cross-architecture regression problem, which is still in its infancy. We highlight how the KD helps the student's learning from the teacher in the regression problem. Additionally, we provide a comparative study of the performance of the NN-based equalizers for both the teacher and the students with different NN architectures. The performance comparison was carried out in terms of the Q-factor, inference speed, and computational complexity. The equalization performance was evaluated using both simulated and experimental data. The 1D-CNN outperformed other NN types as a student model with respect to the Q-factor. The proposed 1D-CNN showed a significant reduction in the inference time compared to the biLSTM while maintaining comparable performance in the experimental data and experiencing only a slight degradation in the Q-factor in the simulated data.
Original languageEnglish
Number of pages10
JournalJournal of Lightwave Technology
Early online date29 Nov 2023
Publication statusE-pub ahead of print - 29 Nov 2023

Bibliographical note

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  • Artificial intelligence
  • machine learning
  • recurrent neural networks
  • parallelization
  • knowledge distillation
  • nonlinear equalizer
  • coherent detection


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