Linear regression method for wide-band flat-gain fiber Raman amplifier pump power automatic configuration

He Wen; Lihua Wei; Wanyi Gu

doi:10.1117/12.635740

9 December 2005 Linear regression method for wide-band flat-gain fiber Raman amplifier pump power automatic configuration

He Wen, Lihua Wei, Wanyi Gu

Author Affiliations +

Proceedings Volume 6021, Optical Transmission, Switching, and Subsystems III; 60211M (2005) https://doi.org/10.1117/12.635740
Event: Asia-Pacific Optical Communications, 2005, Shanghai, China

Abstract

Linear regression method for flat-gain Fiber Raman amplifier (FRA) pump power automatic configuration is proposed. It can give the optimal pump power configuration automatically according to the signal output variation with pump power changes, without any prior knowledge of fiber parameters, like attenuation, effective area, Raman gain factor, and etc. This method is based on the pump power integral model and linear regression theory and is verified by numerical simulation and experiment. The impact on pump configuration caused by power measurement uncertainty is also considered. The results show that deliberate selecting some parameters, like initial pump configuration, pump power variation amount, pump power perturbation times can reduce the impact.

Citation Download Citation

He Wen, Lihua Wei, and Wanyi Gu "Linear regression method for wide-band flat-gain fiber Raman amplifier pump power automatic configuration", Proc. SPIE 6021, Optical Transmission, Switching, and Subsystems III, 60211M (9 December 2005); https://doi.org/10.1117/12.635740

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available