高速移动环境下混叠中继时变信道建模与逼近、参数估计和导频设计

项目名称： 高速移动环境下混叠中继时变信道建模与逼近、参数估计和导频设计

项目编号： No.61201202

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 王公仆

作者单位： 北京交通大学

项目金额： 25万元

中文摘要： 中继通信是当前无线领域研究的热点。信道估计对中继系统性能有着重要的影响。目前对无线中继系统信道估计与导频设计的研究主要针对于时不变或者准静态信道。在实际系统中，任何一个通信节点的移动性，都会导致时变信道。在高速移动环境下，信道的时变特征更加显著。而现有的时变信道估计方法不能应用于采取放大转发模式的无线中继系统混叠时变信道, 导频设计也有待进一步研究。为此，课题针对高速移动环境中的各种无线中继通信系统，研究混叠后的时变信道参数估计和最优导频设计：1）对混叠后的时变信道进行建模分析，系统地分析其统计特性，改进或者创造较好的基扩展模型来逼近混叠后的时变信道；2）设计高精度的信道参数估计算法，推导存在信道估计误差时的混叠时变信道容量或者其下界；3）分别以最小化估计均方误差、最大化信道容量为目标，设计最优导频。课题研究对无线中继系统在实际中运用有着重要的理论研究意义和实际应用价值。

中文关键词： 信道估计；中继系统；无线通信；估计误差；导频设计

英文摘要： Recently relay communication has become an active research field. Like any other wireless communication system, a relay system performs better with better channel estimates. Current channel estimation and training sequence design for relay networks mainly focus on time-invariant or quasi-static channels. However, the source node, the relay nodes and the destination node in practical relay networks can be mobile, which will result in time-varying channel, especially for nodes with high speed. Existing time-varying channel estimation techniques and pilot design cannot be applied in amplify-and-forward (AF) relay networks. Therefore, our research will focus on cascaded channel estimation and pilot design for relay systems in rapidly changing environments. Specifically, we work will include the following three parts: first, we model the cascaded relay channel, analyze its statistical properties, and adapt existing basis expansion models (BEMs) or build new BEM to approximate the cascaded relay channel; second, new estimation algorithms will be proposed to accurately estimate the cascaded relay channel and the channel capacity will be derived in the presence of channel estimation error; finally, optimal pilot design will be suggested to minimize the channel estimation mean square error or maximize the channel capacit

英文关键词： channel estimation；relay systems；wireless communications；estimation error；training sequence design