LGMay 4, 2024
Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network
Chen Shao, Elias Giacoumidis, Patrick Matalla, Jialei Li, Shi Li, Sebastian Randel, Andre Richter, Michael Faerber, Tobias Kaefer
arXiv:2405.02609v12 citationsh-index: 6
Originality Incremental advance
AI Analysis
This addresses signal quality issues in high-speed optical networks, offering incremental improvements for telecommunications.
The paper tackles equalization in 112 Gb/s upstream PON by proposing a Fourier Convolution-based Network, which improves receiver sensitivity by 2 dB and 1 dB over existing methods at a BER of 0.005.
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.