基于叠加训练序列的时变信道估计及预编码信号分离策略研究

项目名称： 基于叠加训练序列的时变信道估计及预编码信号分离策略研究

项目编号： No.61302099

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： 窦高奇

作者单位： 中国人民解放军海军工程大学

项目金额： 26万元

中文摘要： 随着无线频谱资源的日趋紧张和高移动环境的应用需求，无线通信系统设计面临越来越严峻的挑战，如何充分利用有限的频谱资源是现代移动通信必须解决的问题。基于叠加训练的传输方案由于消除了训练序列占用的频带开销而受到广泛关注。然而，采用叠加信号传输方式会带来训练序列与信息符号的相互干扰，直接导致信道估计和检测性能的恶化，如何分离叠加信号进而消除二者的相互干扰是提高系统性能的关键。本项目以叠加信号分离方案设计作为切入点，探究预编码叠加训练方案设计、时变信道模型优化和迭代接收等方面的问题。提出利用多相正交序列来设计预编码叠加信号的新思路；立足函数空间理论，充分挖掘信道先验信息来提高估计性能；最后，从信息论角度综合评价迭代估计与检测方案的性能。研究旨在建立新的发射信号框架和叠加信号分离理论，克服目前叠加训练方案存在的不足，同时为时变衰落信道下其它高效传输系统提供新思路和解决方案。

中文关键词： 叠加训练；仿射预编码；信道估计；干扰抑制；变换域序列

英文摘要： Due to the scarce spectrum resources and high mobile scenarios,the design of wireless communication systems faces more and more challenging. It is of critical importance to utilize the finite spectrum resources efficiently for modern mobile communications. The superimposed training(ST)-based transmission schemes have been attracted much attention without loss of training bandwidth. However, with the superimposed mode,the data-training interference severely deteriorates the performance of channel estimation and data detection.The key task is how to separate the superimposed signals and eliminate the mutual interference at the receiver.In an attempt to design the separable superimposed signals, three specific aspects are involved,such as the design of superimposed training scheme with precoding, optimization parameters for channel model and iterative received processing. First, a novel idea of precoding superimposed mode built from orthogonal polyphase sequences is proposed. Based on the theory of function space, the prior information of time-varying channel is extracted to enhance the estimation performance. Finally, the iterative received schemes are designed and their performance is assessed from the viewpoint of information theory. The new transmitted signal framework and separation theory for superimposed sig

英文关键词： superimposed training；affine precoding；channel estimation；interference mitigation；transform-domain sequences