时频双选信道中基于变分推理的迭代接收技术

项目名称： 时频双选信道中基于变分推理的迭代接收技术

项目编号： No.61201181

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： 武楠

作者单位： 北京理工大学

项目金额： 25万元

中文摘要： 随着人们对"移动性"和"多媒体业务"需求的不断增长，由多径和多普勒效应引起的时间和频率双选择性衰落对无线传输的影响越来越显著，因此其最佳接收机必须进行联合信道估计和检测。本课题将已在机器学习领域得到成功应用的"变分推理"引入时频双选信道的接收机设计，通过最小化与目标函数之间的KL距离，将包含多变量的复杂函数用相对简单且易于因式分解的函数近似。对采用基扩展模型参数化的时频双选信道，研究受到载波频偏和相位噪声影响下基于变分推理的联合信道估计和检测算法，分析不同独立性约束条件对算法性能和复杂度的影响。在时频双选信道的因子图模型上，研究基于变分推理的混合消息传递的规则和条件，设计基于变分消息传递的迭代估计、检测和译码算法，并通过EXIT图和"半分析"方法分析其性能。由于变分推理具有系统性和结构化的特点，本课题的研究可扩展至干扰抑制、多用户检测等问题，对迭代接收机的设计具有重要的理论意义和应用价值。

中文关键词： 时频双选信道；变分推理；迭代接收机；因子图；联合信道估计与检测

英文摘要： Given the ever increasing demand for multimedia service and the high mobility of terminals, the effect of doubly selective fading caused by multipath and Doppler spread on the transmitted signals becomes more prominent. To this end, the optimal receiver has to perform joint channel estimation and data detection. In this study, we propose to use variational inference, which has been successfully applied in machine learning, to the receiver design in time and frequency doubly selective channels (DSCs). By minimizing the Kullback-Leibler (KL) divergence between the target function and the postulated one, variational inference approximates the complicated multi-variant function with a relatively simple one which is much easier to factorize. For DSCs parameterized by basis expansion model (BEM), joint channel estimation and data detection algorithms based on variational inference in the presence of carrier frequency offset (CFO) and phase noise (PHN) are going to be investigated. The effects of using different conditional independent constraints will be compared in terms of performance and computational complexity. On the factor graph representation of DSCs, the application conditions and the rules of using hybrid massage passing based on variational inference will be studied. The joint iterative channel estimation,

英文关键词： Time and Frequency Doubly Selective Channel；Variational Inference；Iterative Receiver；Factor Graphs；Joint Channel Estimation and Detection