# Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network

arXiv: https://arxiv.org/abs/2405.02609 · HTML: https://mlsanity.com/paper/2405.02609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 5 of 5)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Fully-blind Neural Network Based Equalization for Severe Nonlinear Distortions in 112 Gbit/s Passive Optical Networks](https://mlsanity.com/paper/2401.09579/) (2024)
- [A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON](https://mlsanity.com/paper/2405.00720/) (2024)
- [KIGLIS: Smart Networks for Smart Cities](https://mlsanity.com/paper/2106.04549/) (2021)

## Abstract (arXiv)

We experimentally demonstrate a novel, low-complexity Fourier Convolution-based Network (FConvNet) based equalizer for 112 Gb/s upstream PAM4-PON. At a BER of 0.005, FConvNet enhances the receiver sensitivity by 2 and 1 dB compared to a 51-tap Sato equalizer and benchmark machine learning algorithms respectively.