# A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON

arXiv: https://arxiv.org/abs/2405.00720 · HTML: https://mlsanity.com/paper/2405.00720/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://mlsanity.com/paper/2106.09305/) (2021)

## Built on by (most cited first, 2 of 2)

- [Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2405.09557/) (2024)
- [Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network](https://mlsanity.com/paper/2405.02609/) (2024)

## Abstract (arXiv)

A frequency-calibrated SCINet (FC-SCINet) equalizer is proposed for down-stream 100G PON with 28.7 dB path loss. At 5 km, FC-SCINet improves the BER by 88.87% compared to FFE and a 3-layer DNN with 10.57% lower complexity.