Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913341336715264 |
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| author | Shao, Chen Giacoumidis, Elias Matalla, Patrick Li, Jialei Li, Shi Randel, Sebastian Richter, Andre Faerber, Michael Kaefer, Tobias |
| author_facet | Shao, Chen Giacoumidis, Elias Matalla, Patrick Li, Jialei Li, Shi Randel, Sebastian Richter, Andre Faerber, Michael Kaefer, Tobias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02609 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network Shao, Chen Giacoumidis, Elias Matalla, Patrick Li, Jialei Li, Shi Randel, Sebastian Richter, Andre Faerber, Michael Kaefer, Tobias Machine Learning 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. |
| title | Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.02609 |