基于正交频分复用的弹性光网络中信号调制格式识别研究

发布时间：2018-04-30 21:21

本文选题：正交频分复用 + 弹性光网络　；参考：《聊城大学》2017年硕士论文

【摘要】：近年来,随着宽带数据业务的巨大发展以及大规模商用,核心网络的网络流量几乎以每年2倍的速度增长,并且增长趋势日益加快。因此网络供应商应该更新他们的设备来支持高数据速率产品的广泛应用。由于快速增长的通信量需求,要实现未来的光网络需要大容量、低价格的光纤传输系统。而目前的波分复用(WDM,Wavelength Division Multiplexing)技术也将达到了它的极限400Gb/s,为满足未来互联网的需要,光传输和网络技术要向更高效、灵活、可扩展的方向发展。因此为了提高现在大规模应用的WDM网络结构的频谱利用率,专家学者们在现有的网络结构基础上提出了一些新型的网络结构,而基于正交频分复用(OFDM,Orthogonal Frequency Division Multiplexing)的弹性光网络(EON,Elastic Optical Networking),因其具有较大的优势而被认为是的下一代网络结构中当前WDM网络结构的最优替代品。因此,在OFDM-based EON这种新型网络结构中,在先验信息缺乏的条件下,如何进行对多载波的随机参数进行自适应识别关系到这种新型网络结构能否真正大规模应用。在OFDM-based EON中,信号的各种参数均处于未知状态,因此在接收端在缺乏先验信息的条件下,能够对信号进行自适应参数显得十分重要。同时调制格式在信号参数自适应识别的过程中处于前端,因此调制格式识别的准确率与效率关系到接下去的参数识别准确率与效率。因此本文参考借鉴了在其他领域中用于调制格式识别的方法,进行改进并首次在OFDM-based EON这种新型网络结构中进行运用。本文主要的研究内容如下:(1)在OFDM-based EON这种新型网络结构中,在先验信息缺乏的情况下,首次将计算高阶循环累积量的方法引入光OFDM信号的调制格式自适应识别中。在计算过程中,本文得出了常用低高阶调制格式的高阶循环累积量值,并在此基础上给出了调制格式识别区间,并且通过仿真对该调制格式识别区间进行了性能检验。(2)在OFDM-based EON这种新型网络结构中,在先验信息缺乏的情况下,首次引入了星座图聚类并以此为基础提出了一种改进的光OFDM信号的调制格式识别方法。本文利用仿真系统搭建了OFDM-based EON网络结构中的系统模型,并通过仿真验证了该算法的性能。
[Abstract]:In recent years, with the huge development of broadband data services and large-scale commercial, the network traffic of core networks is almost twice as fast as every year, and the increasing trend is accelerating day by day. Therefore, network providers should update their devices to support the wide application of high data rate products. Because of the fast increasing demand of communication traffic, large capacity and low price optical fiber transmission system is needed to realize the future optical network. The current wavelength Division Multiplexing (WDM) technology will reach its limit of 400GB / s. In order to meet the needs of the future Internet, optical transmission and network technology should be more efficient, flexible and scalable. Therefore, in order to improve the spectral efficiency of the WDM network structure, experts and scholars put forward some new network structure based on the existing network structure. The elastic optical network based on orthogonal frequency division multiplexing (OFDM) and orthogonal Frequency Division Multiplexing) is considered to be the best substitute for the current WDM network structure in the next generation because of its advantages. Therefore, in the new network structure of OFDM-based EON, under the condition of lack of prior information, how to self-adaptively identify the random parameters of multi-carrier is related to whether this new network structure can be applied on a large scale. In OFDM-based EON, all the parameters of the signal are unknown, so it is very important to be able to adapt the parameters to the signal without prior information at the receiving end. At the same time, the modulation format is in the front end in the process of adaptive recognition of signal parameters, so the accuracy and efficiency of modulation format recognition are related to the next parameter identification accuracy and efficiency. Therefore, this paper uses the method of modulation format recognition in other fields for reference, and improves it and applies it to the new network structure of OFDM-based EON for the first time. The main contents of this paper are as follows: (1) in the new network structure of OFDM-based EON, the method of calculating high order cyclic cumulants is introduced into the adaptive recognition of modulation format of optical OFDM signals for the first time in the absence of prior information. In the course of calculation, the high order cyclic cumulants of common low order modulation schemes are obtained, and on this basis, the recognition interval of modulation formats is given. And the performance test of the modulation format recognition interval is carried out by simulation. In the new network structure of OFDM-based EON, the prior information is lacking. Constellation clustering is introduced for the first time and an improved modulation format recognition method for optical OFDM signals is proposed. In this paper, the system model of OFDM-based EON network structure is built by simulation system, and the performance of the algorithm is verified by simulation.
【学位授予单位】：聊城大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.1

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