# Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment

arXiv: https://arxiv.org/abs/2205.11284 · HTML: https://mlsanity.com/paper/2205.11284/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 23 May 2022.

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

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision](https://mlsanity.com/paper/1905.03696/) (2019)
- [Low-bit Quantization of Neural Networks for Efficient Inference](https://mlsanity.com/paper/1902.06822/) (2019)
- [Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks](https://mlsanity.com/paper/1909.13144/) (2019)
- [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)

## Built on by (most cited first, 1 of 1)

- [Quantization of Neural Network Equalizers in Optical Fiber Transmission Experiments](https://mlsanity.com/paper/2310.05950/) (2023)

## Abstract (arXiv)

A neural network is quantized for the mitigation of nonlinear and components distortions in a 16-QAM 9x50km dual-polarization fiber transmission experiment. Post-training additive power-of-two quantization at 6 bits incurs a negligible Q-factor penalty. At 5 bits, the model size is reduced by 85%, with 0.8 dB penalty.