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正交频分复用系统中基于子空间跟踪的信道估计
引用本文:李悦,李子,蔡跃明,徐友云.正交频分复用系统中基于子空间跟踪的信道估计[J].上海交通大学学报,2005,39(9):1494-1499.
作者姓名:李悦  李子  蔡跃明  徐友云
作者单位:1. 解放军理工大学,通信工程学院,南京,210007;海军装备研究院,舰船作战系统论证研究所,北京,100073
2. 解放军理工大学,通信工程学院,南京,210007
3. 解放军理工大学,通信工程学院,南京,210007;上海交通大学,电子工程系,上海,200030
基金项目:国家高技术研究发展计划(863)项目(2003AA12331007).
摘    要:基于导频的正交频分复用(OFDM)系统信道估计方法是通过在确定的时频位置发送已知的导频符号来获取信道参数的,通常包括导频位置的最小二乘估计和内插滤波两步。如果能够提高导频位置信道频域特性的估计精度,那么后续内插滤波的精度也就能相应地得到提高,为此,在建立通用参数化信道模型的基础上,提出一种子空间跟踪的改进算法,该算法通过基于Givens旋转的时延子空间跟踪和基于RLS滤波的幅度跟踪,提高了导频符号信道估计的精度,降低了跟踪算法的复杂度。仿真结果表明,基于子空间跟踪的信道估计方法具有较低的复杂度和较高的估计精度。

关 键 词:正交频分复用  信道估计  子空间跟踪  低秩自适应滤波  递归最小二乘
文章编号:1006-2467(2005)09-1494-06
收稿时间:2004-09-14
修稿时间:2004年9月14日

A Channel Estimation Method for Orthogonal Frequency Division Multiplexing Systems by Tracking Delay-Subspace
LI Yue,LI Zi,CAI Yue-ming,XU You-yun.A Channel Estimation Method for Orthogonal Frequency Division Multiplexing Systems by Tracking Delay-Subspace[J].Journal of Shanghai Jiaotong University,2005,39(9):1494-1499.
Authors:LI Yue  LI Zi  CAI Yue-ming  XU You-yun
Institution:LI Yue, LI Zi, CAI Yue-ming, XU You-yun (1. Inst. of Communications Eng. , PLA Univ. of Science
Abstract:Channel parameters in orthogonal frequency division multiplexing (OFDM) systems over fading channels are generally obtained by transmitting pilot symbols in given positions of the time-frequency grid. The pilot-based method usually involves the LS estimation step on the pilot subcarriers and the interpolation step over the entire time-frequency grid. Increasing the accuracy of the pilot estimation will accordingly increase the precision in the interpolation step. This paper proposed an improved subspace-tracking algorithm by the Givens rotation based delay-subspace tracking and the RLS filtering based amplitudes tracking. Performed on the parameterized model of the channel, the simulations prove that the channel (estimation) is more robust and accurate while the complexity of the algorithm is low.
Keywords:orthogonal frequency division multiplexing(OFDM)  channel estimation  subspace tracking  low-rank adaptive filtering  recursive least squares
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