Constellation-Shaped 64QAM Robust against Phase Noise in Intra-Datacenter Networks

Sakano, Jun; Shiraki, Ryuta; Mori, Yojiro; Hasegawa, Hiroshi

doi:10.23919/oecc/psc53152.2022.9850095

Cited by 1 publication

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To deal with the signal impairments that cause non-uniform symbol distribution on the complex plane, e.g., intense fiber nonlinearity and residual laser phase noise, ML-based PCS and GCS methods have been proposed [41][42][43][44][45][46][47][48][49][50][51][52][53]. [45] proposed an NN-based PCS method to compensate the coupled nonlinearity between transceivers and optical fibers.…”

Section: Digital Signal Processing Based On MLmentioning

confidence: 99%

“…An AE model that normalizes the encoder output so as to satisfy the peak-power constraints enhances the QoT in unamplified system. [44] presented a joint optimization scheme applicable to most systems; it drops the differentiability usually necessary for AE. By using the local-optimization approach for the loop-in-loop optimization process, transmitter-side and receiver-side DSP circuits are jointly optimized.…”

Section: Digital Signal Processing Based On MLmentioning

confidence: 99%

See 1 more Smart Citation

Optical path management based on machine learning for optical networks

Shiraki

Mori

Hasegawa

2023

Next-Generation Optical Communication: Components, Sub-Systems, and Systems XII

View full text Add to dashboard Cite

The popularity of high-capacity communication services such as video streaming and cloud computing has accelerated the growth in IP traffic. In order to effectively manage and maintain networking systems, various intelligent technologies based on software-defined networking (SDN) have been widely studied. An SDN system that offers flexible optical path management exploiting optical performance monitoring, digital signal processing, and resource allocation is expected to realize higher capacity networks by lowering margins needed to offset system uncertainty. In this paper, we provide a comprehensive survey of optical path management schemes based on machine learning.

show abstract