A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914780538732544 |
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| author | Shao, Chen Giacoumidis, Elias Li, Shi Li, Jialei Faerber, Michael Kaefer, Tobias Richter, Andre |
| author_facet | Shao, Chen Giacoumidis, Elias Li, Shi Li, Jialei Faerber, Michael Kaefer, Tobias Richter, Andre |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00720 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON Shao, Chen Giacoumidis, Elias Li, Shi Li, Jialei Faerber, Michael Kaefer, Tobias Richter, Andre Signal Processing Machine Learning 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. |
| title | A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2405.00720 |