Maximum Likelihood Estimation of Wiener Phase Noise Variance in Coherent Optical Systems

被引:5
|
作者
Du, Xinwei [1 ]
Wang, Qian [2 ]
Kam, Pooi-Yuen [3 ,4 ]
机构
[1] BNU HKBU UnitedInternat Coll, Guangdong Prov Key Lab Interdisciplinary Res & App, Zhuhai 519087, Peoples R China
[2] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310023, Peoples R China
[3] Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China
[4] NUS Res Inst NUSRI, Ctr Adv Microelect Devices, Suzhou 215123, Peoples R China
关键词
Phase noise variance; laser linewidth; maximum likelihood; machine learning; coherent optical communications; LASER-LINEWIDTH; FREQUENCY OFFSET; RESOLUTION; TRACKING; FILTER;
D O I
10.1109/JLT.2024.3357289
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The estimation of laser linewidth or phase noise variance is of great significance for the applications including coherent optical communications, optical fiber sensing, quantum optics, etc., to ensure the detection sensitivity and accuracy. In coherent optical communications, the Wiener phase noise introduced by the non-zero laser linewidth can lead to the rotation of constellations of the transmitted signal, which results in severe signal detection performance degradation. Many algorithms require the prior information of phase noise variance to achieve accurate phase recovery. In this article, we propose a maximum likelihood (ML) algorithm for the estimation of Wiener phase noise variance or laser linewidth, which is based on the amplitude and phase-form of the noisy received signal model together with the use of the best, linearized, additive observation phase noise (AOPN) model due to additive white Gaussian noise (AWGN). The closed-form expression of ML estimates of carrier phase offset and Wiener phase noise variance is derived. We also verified theoretically that the obtained ML estimate of Wiener phase noise variance is unbiased and close to the actual variance with probability arbitrarily close to 1, as the sample size N tends to infinity. The proposed ML estimator is shown to have accurate estimation performance with low computational complexity.
引用
收藏
页码:3163 / 3173
页数:11
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