# Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems

arXiv: https://arxiv.org/abs/2206.12180 · HTML: https://mlsanity.com/paper/2206.12180/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [An Overview on Application of Machine Learning Techniques in Optical Networks](https://mlsanity.com/paper/1803.07976/) (2018)
- [Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2103.08212/) (2021)
- [Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation](https://mlsanity.com/paper/2001.09277/) (2020)
- [Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2104.05081/) (2021)
- [End-to-end optimization of coherent optical communications over the split-step Fourier method guided by the nonlinear Fourier transform theory](https://mlsanity.com/paper/2009.05324/) (2020)
- [Ultralow complexity long short-term memory network for fiber nonlinearity mitigation in coherent optical communication systems](https://mlsanity.com/paper/2108.10212/) (2021)
- [Mitigating Nonlinear Interference by Limiting Energy Variations in Sphere Shaping](https://mlsanity.com/paper/2201.00674/) (2022)

## Built on by (most cited first, 4 of 4)

- [FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training](https://mlsanity.com/paper/2212.03515/) (2022)
- [Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementation](https://mlsanity.com/paper/2304.06987/) (2023)
- [Hardware Realization of Nonlinear Activation Functions for NN-based Optical Equalizers](https://mlsanity.com/paper/2305.09495/) (2023)
- [Real-Time FPGA Demonstrator of ANN-Based Equalization for Optical Communications](https://mlsanity.com/paper/2402.15288/) (2024)

## Abstract (arXiv)

For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity compensation are implemented in an FPGA, with a level of complexity comparable to that of a dispersion equalizer. We demonstrate that the NN-based equalizers can outperform a 1 step-per-span DBP.