基于叠加训练（ST）信道估计的相干光正交频分复用系统研究

项目名称： 基于叠加训练（ST）信道估计的相干光正交频分复用系统研究

项目编号： No.61307090

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 郭昌建

作者单位： 华南师范大学

项目金额： 26万元

中文摘要： 传统的相干光正交频分复用（CO-OFDM）系统用在信道和载波相位估计、抗码间干扰上的带宽开销巨大，降低了频谱效率。为解决这一问题，本项目将叠加训练序列（ST）信道估计引入CO-OFDM系统中，研究其在CO-OFDM系统的应用和优化。ST算法将训练序列算术叠加在信号上,因而不增加系统的带宽开销，提高了频谱效率。我们首先将研究基于ST和注入导频的信道和载波相位联合估计方案，并研究通过迭代反馈消除数据信号对训练序列的干扰，从而增加信道估计精度的算法；之后，我们将研究CO-OFDM和偏振复用（PDM）CO-OFDM系统的数据相关叠加训练（DDST）算法，研究其在信道和载波相位估计的应用，从而进一步提高频谱效率；我们将利用现有的实验平台，完成以上算法的实验验证，并实现一个速率为400 Gb/s（2偏振×5超通道×10 GBaud/s，16-QAM）、传输距离1000公里的PDM-CO-OFDM系统。

中文关键词： 叠加训练；信道估计；色散补偿；超奈奎斯特镜像混叠；非线性补偿

英文摘要： In a conventional CO-OFDM system, a large portion of the bandwidth is consumed by channel and carrier phase estimation as well as anti-ISI cyclic prefixes, which significantly decreases its spectra efficiency. To address this problem, a 3-year research project on channel estimation using superimposed training (ST) in coherent optical OFDM (CO-OFDM) systems is proposed. One major advantage of using ST based channel estimation is that no extra bandwidth is needed, since the periodic ST sequences are arithmetically added to the OFDM signals, while valuable bandwidth or time slot is required for conventional pilot-aided CO-OFDM systems using preambles or inserted subcarriers. Therefore, higher spectra efficiency is expected for ST based CO-OFDM systems. In this proposal, emphasis will be put on its enabling technologies as well as its experimental demonstrations and optimization approaches. Firstly, a joint channel and carrier phase estimation scheme using ST and inserted subcarriers is proposed, with a decision feed-back algorithm to enhance its estimated channel accuracy. Secondly, an improved ST algorithm, namely data-dependent ST (DDST) algorithm, is introduced in CO-OFDM systems for both channel and carrier phase estimation to further increase the spectra efficiency. Thirdly, the abovementioned algorithms will

英文关键词： superimposed training；channel estimation；chromatic dispersion compensation；super-Nyquist image aliasing；nonlinearity compensation