Conclusion
We have demonstrated a data-driven TDECQ assessment scheme based on L-SNN. In comparison with existing DL-based schemes, the proposed L-SNN can achieve the lowest computation complexity with only 210 multiplications. The MAE of the L-SNN scheme for 25 and 50 Gbaud PAM-4 optical signals is experimentally verified to be 0.13 and 0.15 dB, respectively, over the TDECQ range of 1.5–4.0 dB, which has reached the accuracy threshold of 0.25 dB recommended by the IEEE standard.
This is a preview of subscription content, log in via an institution to check access.
References
IEEE Standard for Ethernet. Amendment 10: Media Access Control Parameters, Physical Layers, and Management Parameters for 200 Gb/s and 400 Gb/s Operation. 802.3bs-2017. https://ieeexplore.ieee.org/servlet/opac?punumber=8207823
Khan F N, Fan Q R, Lu C, et al. An optical communication’s perspective on machine learning and its applications. J Lightwave Technol, 2019, 37: 493–516
Varughese S, Garon D A, Melgar A, et al. Accelerating TDECQ assessments using convolutional neural networks. In: Proceedings of the Optical Fiber Communications Conference and Exhibition (OFC), 2020
Guan X, Yang Y, Li J J, et al. Mind the remainder: Taylor’s theorem view on recurrent neural networks. IEEE Trans Neural Netw Learn Syst, 2022, 33: 1507–1519
Varughese S, Melgar A, Thomas V A, et al. Accelerating assessments of optical components using machine learning: TDECQ as demonstrated example. J Lightwave Technol, 2021, 39: 64–72
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. 62025502) and Guangdong Introducing Innovative and Entrepreneurial Teams of the Pearl River Talent Recruitment Program (Grant No. 2021ZT09X044).
Author information
Authors and Affiliations
Corresponding author
Additional information
Supporting information Appendixes A and B. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.
Rights and permissions
About this article
Cite this article
Xiang, J., Chen, Z., Cheng, Y. et al. Linear shallow neural network to accelerate transmitter dispersion eye closure quaternary (TDECQ) assessment. Sci. China Inf. Sci. 67, 149301 (2024). https://doi.org/10.1007/s11432-023-3947-8
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-023-3947-8