Srivallapanondh, Sasipim, Freire, Pedro J., Spinnler, Bernhard, Costa, Nelson, Napoli, Antonio, Turitsyn, Sergei K. and Prilepsky, Jaroslaw E. (2023). Parallelization of Recurrent Neural Network-Based Equalizer for Coherent Optical Systems via Knowledge Distillation. Journal of Lightwave Technology ,
Abstract
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.
Publication DOI: | https://doi.org/10.1109/jlt.2023.3337604 |
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Divisions: | College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT) College of Engineering & Physical Sciences Aston University (General) |
Funding Information: | This work has received funding from the EU Horizon 2020 program under the Marie Skłodowska-Curie grant agreement No. 956713 (MEN- TOR). SKT acknowledges the support of the EPSRC project TRANSNET (EP/R035342/1). Bernhard Spinnler, Nelson Costa, Antonio Nap |
Additional Information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0 |
Uncontrolled Keywords: | Artificial intelligence,machine learning,recurrent neural networks,parallelization,knowledge distillation,nonlinear equalizer,coherent detection |
Publication ISSN: | 0733-8724 |
Last Modified: | 02 Dec 2024 09:02 |
Date Deposited: | 15 Dec 2023 12:28 |
Full Text Link: | |
Related URLs: |
https://ieeexpl ... cument/10333336
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2023-11-29 |
Published Online Date: | 2023-11-29 |
Accepted Date: | 2023-11-01 |
Authors: |
Srivallapanondh, Sasipim
Freire, Pedro J. ( 0000-0003-3145-1018) Spinnler, Bernhard Costa, Nelson Napoli, Antonio Turitsyn, Sergei K. ( 0000-0003-0101-3834) Prilepsky, Jaroslaw E. ( 0000-0002-3035-4112) |