# Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection

arXiv: https://arxiv.org/abs/2212.04569 · HTML: https://mlsanity.com/paper/2212.04569/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation.
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Placed in

- Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/)

## Builds on (most cited first, 6 of 6)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2103.08212/) (2021)
- [Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks](https://mlsanity.com/paper/2001.11802/) (2020)
- [Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation](https://mlsanity.com/paper/2208.12866/) (2022)
- [Performance and Complexity Analysis of bi-directional Recurrent Neural Network Models vs. Volterra Nonlinear Equalizers in Digital Coherent Systems](https://mlsanity.com/paper/2103.03832/) (2021)

## Built on by (most cited first, 3 of 3)

- [Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2405.09557/) (2024)
- [Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2307.05374/) (2023)
- [Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signaling](https://mlsanity.com/paper/2609.07762/) (2026)

## 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.