Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection
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Abstract (arXiv)
To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feedforward structure. The latter shows 38% latency decrease, while impacting the Q-factor by only 0.5dB.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 8 Dec 2022.
Builds on
6 of 13 references.
References · 6
- 2015Distilling the Knowledge in a Neural Networkcited by 10,076
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
- 2021Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systemscited by 17
- 2020Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networkscited by 8
- 2021Performance and Complexity Analysis of bi-directional Recurrent Neural Network Models vs. Volterra Nonlinear Equalizers in Digital Coherent Systemscited by 6
- 2022Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementationcited by 6
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.
3 papers.
- 2024Machine Learning in Short-Reach Optical Systems: A Comprehensive Surveycited by 1
- 2023Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systemscited by 0
- 2026Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signalingcited by 0
- 2026Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signaling7 Sep 2026
- 2024Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey2 May 2024
- 2023Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems4 Jul 2023