Novel Low Complexity Decision Feedback Volterra-Inspired Neural Network Equalizer for Net-355- $\text{Gbs }/\lambda$ Transmission in Short-Reach IMDD Optical System
A novel decision feedback VINN equalizer is utilized to realize low-complexity nonlinear equalization. It is a white-box machine learning (ML) algorithm guided by a conventional physical model. Based on this equalizer, we experimentally demonstrate a net-355-Gbs/ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> PAM-8 transmission in an IMDD short-reach system.
Authors
- Lilin Yi (ORCID: https://orcid.org/0000-0002-6039-9063)
- Ming Jiang
- An Yan (ORCID: https://orcid.org/0009-0009-2778-3803)
- Dezhi Zhang (ORCID: https://orcid.org/0000-0001-9014-5315)
- Junwen Zhang (ORCID: https://orcid.org/0000-0001-8041-1608)
- Luyao Huang
Institutions
- Shanghai Jiao Tong University (CN)
- Fudan University (CN)
- China Telecom (China) (CN)
Publication Details
- Published
- 2024-11-02
- DOI
- https://doi.org/10.1109/acp/ipoc63121.2024.10809763
- Citations
- 2
- Primary Topic
- Optical Network Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.42