Summary of Advanced Equalization in 112 Gb/s Upstream Pon Using a Novel Fourier Convolution-based Network, by Chen Shao et al.
Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network
by Chen Shao, Elias Giacoumidis, Patrick Matalla, Jialei Li, Shi Li, Sebastian Randel, Andre Richter, Michael Faerber, Tobias Kaefer
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel Fourier Convolution-based Network (FConvNet) proposed in this paper demonstrates a low-complexity equalizer for 112 Gb/s upstream PAM4-PON. This equalizer enhances receiver sensitivity by up to 3 dB compared to existing methods, such as the 51-tap Sato equalizer and benchmark machine learning algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This equalizer is designed for high-speed optical communication systems. It uses a novel combination of Fourier Convolution-based Network (FConvNet) architecture and low-complexity PAM4-PON receiver sensitivity enhancement. The results show that FConvNet outperforms existing methods in terms of receiver sensitivity. |
Keywords
» Artificial intelligence » Machine learning
