基于Stokes空间参量的认知光接收机的关键技术研究

发布时间：2018-04-28 23:26

本文选题：Stokes空间 + 认知光网络　；参考：《中山大学》2017年硕士论文

【摘要】：随着用户需求的不断增长,未来光纤通信网络的发展呈现高突发性、服务多样化以及智能化的特点。认知光网络,因其能够根据具体环境和用户需求灵活地分配带宽以提高网络资源效率,被看作是下一代光网络的重要选择之一。为了提高认知光网络的快速适配的能力,往往需要光接收机提前获取信号的某些相关参数,如调制格式、光信噪比,以便优化后续的信号处理和解调过程。但是在光纤通信系统中,这些参数的获取或估计往往受到相干光接收机大相位噪声,光纤偏振相关损伤和非线性损伤的影响。近年来,基于Stokes空间参量的相干光信号分析,由于不依赖于接收信号的偏振旋转和混合,相干光接收机引入的大频率偏移和相位噪声,而受到广泛关注。本文主要基于Stokes空间参量的分析方法,对认知相干光接收机中的关键技术——调制格式识别以及光信噪比监测展开研究,主要研究内容及创新点如下:(1)通过用MATLAB进行仿真实验,探究了光纤信道的一些非理想因素对信号在Stokes空间分布的影响;(2)设计并提出了一个基于Stokes空间参量和减法聚类的调制格式识别算法。该算法可以实现对5种常见调制格式(BPSK,QPSK,8PSK,8QAM,16QAM)的正确识别;同已有的基于Stokes空间参量的调制格式识别方法比较,该算法具有较低的复杂度和较好的噪声容忍度;(3)实现了一种基于Stokes空间参量和人工神经网络的光信噪比监测算法。此方法根据不同光信噪比下信号在Stokes空间最小二乘平面上星座点弥散度不同,基于人工神经网络算法学习二者之间的映射关系,进而估算光信噪比。仿真结果表明在OSNR大于5d B的时候能实现0.5d B以内的精确度。相比于一般的函数拟合方法,这种基于人工神经网络的算法具有更高的计算精度;(4)搭建长距离偏振复用相干光通信系统,对以上两种算法进行验证和评估。在调制格式识别的实验中实现了对QPSK和16QAM信号在背靠背传输以及长距离传输条件下的正确识别,在光信噪比监测的实验中对QPSK信号的数据实现了较高精度的监测结果。
[Abstract]:With the increasing demand of users, the development of optical fiber communication network in the future is characterized by high burst, diversified service and intelligence. Cognitive optical networks are regarded as one of the important options for next generation optical networks because they can flexibly allocate bandwidth according to specific environment and user needs to improve network resource efficiency. In order to improve the ability of fast adaptation of cognitive optical networks, it is often necessary for optical receivers to obtain some related parameters of signals in advance, such as modulation format, optical signal-to-noise ratio (SNR), so as to optimize the subsequent signal processing and demodulation process. However, in optical fiber communication systems, the acquisition and estimation of these parameters are often affected by large phase noise, polarization dependent damage and nonlinear damage of coherent optical receivers. In recent years, the analysis of coherent optical signals based on Stokes spatial parameters has attracted wide attention due to the large frequency offset and phase noise introduced by coherent optical receivers, which do not depend on the polarization rotation and mixing of received signals. Based on the analysis method of Stokes spatial parameters, this paper studies the key techniques of cognitive coherent optical receiver, modulation format recognition and optical signal-to-noise ratio monitoring. The main research contents and innovations are as follows: 1) Simulation experiments are carried out with MATLAB. In this paper, the influence of some non-ideal factors on the spatial distribution of signal in Stokes is discussed, and a modulation format recognition algorithm based on Stokes spatial parameter and subtraction clustering is proposed. This algorithm can realize the correct recognition of five common modulation formats: BPSK, QPSK, 8PSK, 8QAM16QAM, and compare with the existing modulation format recognition methods based on Stokes spatial parameters. The algorithm has lower complexity and better noise tolerance. It implements an optical signal-to-noise ratio (SNR) monitoring algorithm based on Stokes spatial parameters and artificial neural networks. This method is based on artificial neural network algorithm to study the mapping relationship between constellation points in Stokes space and estimate the optical signal-to-noise ratio (SNR) according to the different optical signal-to-noise ratio (SNR) signal to noise ratio (OSNR) signal to noise ratio (OSNR). The simulation results show that the accuracy of 0.5 dB can be achieved when the OSNR is larger than 5 dB. Compared with the general function fitting method, the algorithm based on artificial neural network has a higher computational accuracy. (4) A long distance polarization multiplexing coherent optical communication system is built, and the above two algorithms are verified and evaluated. In the experiment of modulation format recognition, the correct recognition of QPSK and 16QAM signals under the condition of back-to-back transmission and long distance transmission is realized. In the experiment of optical signal-to-noise ratio monitoring, the data of QPSK signal are monitored with high accuracy.
【学位授予单位】：中山大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.1

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