【作者】 王磊；

【导师】 高明义；

【作者基本信息】 苏州大学 ， 信息与通信工程， 2021， 硕士

【摘要】 光纤通信通过采用波分复用和偏振分复用等多种复用技术,利用高阶正交幅度调制或扩展通信带宽等方式来提升系统的传输容量以应对日益增长的数据需求。但是在长距离、高带宽的骨干网络中,由克尔非线性带来的非线性香农极限是制约容量增长的重要因素。因此,必须使用非线性补偿技术来缓解光纤通信系统中的非线性损伤。光学相位共轭器,通过信号光与空闲光之间的相位共轭关系来实现对光纤链路中累积非线性损伤的补偿,而人工神经网络可以通过对受非线性干扰后信号星座图的学习来模拟信号传输前后的非线性关系,从而实现对光纤非线性的补偿。本文研究分析了上述两种非线性补偿技术并针对其中的问题提出了高能效的光学相位共轭器和光电融合式非线性均衡器两种方法来分别补偿单信道和波分复用系统中64阶正交幅度调制信号的非线性损伤。提出的两种方法具有低成本,高能效,易于部署,强鲁棒性和低复杂度等特点,对于未来低成本的宽带动态光网络具有很好的应用前景。更多还原

【Abstract】 In order to meet the increasing demand for traffic,optical fiber communication community are trying to enhance the transmission capacity of fiber optic systems by employing various multiplexing techniques such as wavelength division multiplexing(WDM)and polarization division multiplexing(PDM),using advanced modulation formats such as higher-order quadrature amplitude modulation(QAM)or extending the communication bandwidth to other bands and so on.However,the nonlinear Shannon limit of fiber nonlinearity caused by Kerr effects is a big challenge limiting the capacity in the backbone optical network with long distance and high bandwidth.Consequently,nonlinear compensation techniques are indispensable to mitigate nonlinear impairments in fiber optical communication systems.Optical phase conjugation(OPC),by means of the phase conjugation relationship between signal and idler,is used to compensate the accumulated nonlinear impairments in fiber transmission links.Meanwhile,artificial neural networks can model the nonlinear relationship before and after signal transmission by learning the constellation diagram of signal disturbed by fiber nonlinearities,so as to achieve the nonlinearity compensation.In this thesis,the abovementioned two nonlinear compensation techniques are analyzed and two methods are proposed to compensate the fiber nonlinearity of 64-QAM signals in single-channel and WDM systems,respectively,by means of an energy-efficient OPC and a photoelectric nonlinear equalizer.The proposed two methods are characterized by low cost,high energy efficiency,easy deployment strong robustness and low complexity,which are promising for future applications of low-cost broadband dynamic optical networks.更多还原