基于卡尔曼滤波的光信号偏振态和载波恢复技术研究
发布时间：2018-11-16 18:21
【摘要】：将先进调制格式、偏振复用、相干接收、数字信号处理技术相结合是最具潜力提高下一代光通信网络频谱利用率以及传输距离的方案之一。高性能的信号处理算法可以极大改善接收机性能。本文结合当下研究热点,围绕基于卡尔曼滤波器的相干光接收机信号处理技术在偏振态恢复、频偏估计以及载波相位恢复方面的应用进行了深入系统的研究,研究过程中使用仿真与实验相结合的方法,讨论了这三种应用环境中基于卡尔曼滤波的相干光接收机数字信号处理技术性能。在对偏振态恢复技术的研究中,首先在传统的基于扩展卡尔曼滤波器的PDM-QPSK信号偏振态和载波相位同时跟踪技术基础上,对滤波器进行了双测量方程的扩展,使其可以用于PDM-16QAM的偏振态和载波相位跟踪,并分别讨论了偏振态旋转、相位噪声、剩余频偏、剩余色散、偏振模色散以及调优参量等对滤波器性能的影响;其次为了解决偏振态和载波相位同时跟踪的扩展卡尔曼滤波方法频偏依赖的问题,本文提出一种基于半径判决辅助的线性卡尔曼滤波器形式用于接收信号的偏振态跟踪,对PDM-QPSK信号使用单测量方程,对PDM-16QAM信号使用双测量方程,实验结果表明方法具有收敛速度快,可跟踪的偏振态旋转速率高,运算相对简单的优点;随着研究的深入,本文发现针对PDM-QPSK的线性卡尔曼偏振态跟踪方法可以在不改变滤波器结构只需适当调整调优参数的情况下直接应用于PDM-16QAM、PDM-64QAM甚至PDM-QPSK/16QAM混合传输信号的偏振态跟踪,这意味着算法具有“全盲”特性,并且具有更小的运算复杂度。在对频偏估计技术的研究中,在从“角逼近”和“点逼近”两个角度研究了的基于卡尔曼滤波的频偏估计技术,并在基于训练序列去除调制相位和基于M-th power去除调制相位两种情况下讨论了算法的性能,最后将以上四种卡尔曼滤波方法与传统的ViterbiViterbi方法以及Morelli频域峰值法进行了性能对比。在对载波相位恢复技术的研究中,本文研究了QPSK信号和16QAM信号的卡尔曼滤波载波相位恢复方法,仿真结果表明对于16QAM信号来说,双方程结构具有比单方程结构高的估计精度。最后本文讨论了QPSK信号和16QAM信号下卡尔曼滤波器的载波相位恢复精度和线宽容忍性。本文结果对于卡尔曼滤波器在相干光接收机数字信号处理技术中的应用具有较强推动作用和实际意义,对卡尔曼滤波器在偏振态恢复、频偏估计以及载波相位恢复三种应用中的性能提供了实际参考。
[Abstract]:The combination of advanced modulation format, polarization multiplexing, coherent reception and digital signal processing is one of the most promising schemes to improve the spectrum efficiency and transmission distance of the next generation optical communication network. High performance signal processing algorithm can greatly improve receiver performance. Combined with the current research focus, this paper focuses on the application of coherent optical receiver signal processing technology based on Kalman filter in polarization state recovery, frequency offset estimation and carrier phase recovery. The digital signal processing performance of coherent optical receiver based on Kalman filter in these three application environments is discussed by combining simulation and experiment. In the research of polarization recovery technology, firstly, based on the traditional polarization state and carrier phase tracking technology of PDM-QPSK signal based on extended Kalman filter, the dual measurement equation is extended. The effects of polarization rotation, phase noise, residual frequency offset, residual dispersion, polarization mode dispersion and tuning parameters on the filter performance are discussed. Secondly, in order to solve the problem of frequency offset dependence of extended Kalman filter with polarization state and carrier phase tracking simultaneously, a linear Kalman filter based on radius decision is proposed for polarization tracking of received signals. Single measurement equation is used for PDM-QPSK signal and double measurement equation is used for PDM-16QAM signal. The experimental results show that the method has the advantages of fast convergence, high tracking polarization rotation rate and relatively simple operation. With the development of the research, it is found that the linear Kalman polarization tracking method for PDM-QPSK can be directly applied to PDM-16QAM, without changing the filter structure and only adjusting the tuning parameters properly. Polarization tracking of PDM-64QAM or even PDM-QPSK/16QAM hybrid signals means that the algorithm is "all-blind" and has less computational complexity. In the research of frequency offset estimation technology, the frequency offset estimation technology based on Kalman filter is studied from angle approximation and point approximation. The performance of the algorithm is discussed in two cases: the modulation phase is removed based on training sequence and the modulation phase is removed based on M-th power. Finally, the performance of the above four Kalman filtering methods is compared with the traditional ViterbiViterbi method and the Morelli frequency domain peak value method. In the research of carrier phase recovery technique, the Kalman filter method of QPSK signal and 16QAM signal is studied in this paper. The simulation results show that the double equation structure has higher estimation accuracy than the single equation structure for 16QAM signal. Finally, the carrier phase recovery accuracy and linewidth tolerance of Kalman filter in QPSK signal and 16QAM signal are discussed. The results in this paper have a strong impetus and practical significance for the application of Kalman filter in the digital signal processing technology of coherent optical receiver, and it is also useful for the polarization recovery of Kalman filter. The performance of frequency offset estimation and carrier phase recovery provides practical reference.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN713;TN911.7
本文编号：2336272
[Abstract]:The combination of advanced modulation format, polarization multiplexing, coherent reception and digital signal processing is one of the most promising schemes to improve the spectrum efficiency and transmission distance of the next generation optical communication network. High performance signal processing algorithm can greatly improve receiver performance. Combined with the current research focus, this paper focuses on the application of coherent optical receiver signal processing technology based on Kalman filter in polarization state recovery, frequency offset estimation and carrier phase recovery. The digital signal processing performance of coherent optical receiver based on Kalman filter in these three application environments is discussed by combining simulation and experiment. In the research of polarization recovery technology, firstly, based on the traditional polarization state and carrier phase tracking technology of PDM-QPSK signal based on extended Kalman filter, the dual measurement equation is extended. The effects of polarization rotation, phase noise, residual frequency offset, residual dispersion, polarization mode dispersion and tuning parameters on the filter performance are discussed. Secondly, in order to solve the problem of frequency offset dependence of extended Kalman filter with polarization state and carrier phase tracking simultaneously, a linear Kalman filter based on radius decision is proposed for polarization tracking of received signals. Single measurement equation is used for PDM-QPSK signal and double measurement equation is used for PDM-16QAM signal. The experimental results show that the method has the advantages of fast convergence, high tracking polarization rotation rate and relatively simple operation. With the development of the research, it is found that the linear Kalman polarization tracking method for PDM-QPSK can be directly applied to PDM-16QAM, without changing the filter structure and only adjusting the tuning parameters properly. Polarization tracking of PDM-64QAM or even PDM-QPSK/16QAM hybrid signals means that the algorithm is "all-blind" and has less computational complexity. In the research of frequency offset estimation technology, the frequency offset estimation technology based on Kalman filter is studied from angle approximation and point approximation. The performance of the algorithm is discussed in two cases: the modulation phase is removed based on training sequence and the modulation phase is removed based on M-th power. Finally, the performance of the above four Kalman filtering methods is compared with the traditional ViterbiViterbi method and the Morelli frequency domain peak value method. In the research of carrier phase recovery technique, the Kalman filter method of QPSK signal and 16QAM signal is studied in this paper. The simulation results show that the double equation structure has higher estimation accuracy than the single equation structure for 16QAM signal. Finally, the carrier phase recovery accuracy and linewidth tolerance of Kalman filter in QPSK signal and 16QAM signal are discussed. The results in this paper have a strong impetus and practical significance for the application of Kalman filter in the digital signal processing technology of coherent optical receiver, and it is also useful for the polarization recovery of Kalman filter. The performance of frequency offset estimation and carrier phase recovery provides practical reference.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN713;TN911.7
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