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Chin. Opt. Lett.
 Home  List of Issues    Issue 06 , Vol. 16 , 2018    10.3788/COL201816.060601


Flexible polarization demultiplexing method based on an adaptive process noise covariance Kalman filter
Jun Ge, Lianshan Yan, Anlin Yi, Yan Pan, Lin Jiang, Liangliang Dai, Wei Pan, and Bin Luo
Center for Information Photonics &
Communications, School of Information Science and Technology, [Southwest Jiaotong University], Chengdu 610031, China

Chin. Opt. Lett., 2018, 16(06): pp.060601

DOI:10.3788/COL201816.060601
Topic:Fiber optics and optical communication
Keywords(OCIS Code): 060.0660  060.1660  060.2330  

Abstract
A flexible polarization demultiplexing method based on an adaptive Kalman filter (AKF) is proposed in which the process noise covariance has been estimated adaptively. The proposed method may significantly improve the adaptive capability of an extended Kalman filter (EKF) by adaptively estimating the unknown process noise covariance. Compared to the conventional EKF, the proposed method can avoid the tedious and time consuming parameter-by-parameter tuning operations. The effectiveness of this method is confirmed experimentally in 128 Gb/s 16QAM polarization-division-multiplexing (PDM) coherent optical transmission systems. The results illustrate that our proposed AKF has a better tracking accuracy and a faster convergence (about 4 times quicker) compared to a conventional algorithm with optimal process noise covariance.

Copyright: © 2003-2012 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Received:2018/1/8
Accepted:2018/4/16
Posted online:2018/5/25

Get Citation: Jun Ge, Lianshan Yan, Anlin Yi, Yan Pan, Lin Jiang, Liangliang Dai, Wei Pan, and Bin Luo, "Flexible polarization demultiplexing method based on an adaptive process noise covariance Kalman filter," Chin. Opt. Lett. 16(06), 060601(2018)

Note: This work was supported by the National Natural Science Foundation of China (NSFC) (Nos. 61335005, 61325023, and 61401378).



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