Accurate Prediction Of Quality Of Transmission With Dynamically Configurable Optical Impairment Model

Martin Bouda,Shoichiro Oda,Olga Vasilieva,Masatake Miyabe,Setsuo Yoshida,Toru Katagiri,Yasuhiko Aoki,Takeshi Hoshida,Tadashi Ikeuchi

2017 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION (OFC)（2017）

Cited 9|Views35

No score

Abstract

We propose a dynamically configurable optical impairment model for a physical layer abstraction enabling physical parameters learning in multi-vendor networks. We experimentally demonstrate quality of transmission prediction in mesh networks with 0.6 dB Q-factor accuracy.

Translated text

Key words

Q-factor accuracy,mesh networks,multivendor networks,physical parameter learning,physical layer abstraction,dynamically configurable optical impairment model,quality-of-transmission

AI Read Science

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Chat Paper

Summary is being generated by the instructions you defined