Doubly Selective Channel Estimation Using Exponential Basis Models and Subblock Tracking
Doubly Selective Channel Estimation Using Exponential Basis Models and Subblock Tracking
复制标题
DOI：
10.1109/tsp.2009.2036047
复制
发表时间：
2010-03
影响因子：
5.4
通讯作者：
Shuangchi He;Jitendra Tugnait
中科院分区：
文献类型：
--
作者：
Shuangchi He;Jitendra Tugnait
针对双选择性信道，提出了一种新颖的自适应信道估计方法的三个版本，该方法利用过采样复数指数基扩展模型 (CE-BEM)，其中我们跟踪 BEM 系数而不是信道抽头增益。由于已知的指数基函数在 CE-BEM 中很好地捕获了通道的时变性质，因此（未知）BEM 系数的时间变化可能比通道的时间变化慢得多，因此更方便跟踪。我们提出了一种使用时分复用（TM）周期性传输训练符号的 BEM 系数的“子块式”跟踪方案。研究了三种自适应算法来进行 BEM 系数跟踪，其中包括基于假定的 BEM 系数自回归 (AR) 模型的卡尔曼滤波方案，以及两种不需要任何 BEM 系数模型的递归最小二乘 (RLS) 方案。仿真示例说明了我们的方法相对于几种现有的双选择性信道估计器的优越性能。
Three versions of a novel adaptive channel estimation approach, exploiting the over-sampled complex exponential basis expansion model (CE-BEM), is presented for doubly selective channels, where we track the BEM coefficients rather than the channel tap gains. Since the time-varying nature of the channel is well captured in the CE-BEM by the known exponential basis functions, the time variations of the (unknown) BEM coefficients are likely much slower than those of the channel, and thus more convenient to track. We propose a ¿subblockwise¿ tracking scheme for the BEM coefficients using time-multiplexed (TM) periodically transmitted training symbols. Three adaptive algorithms, including a Kalman filtering scheme based on an assumed autoregressive (AR) model of the BEM coefficients, and two recursive least-squares (RLS) schemes not requiring any model for the BEM coefficients, are investigated for BEM coefficient tracking. Simulation examples illustrate the superior performance of our approach over several existing doubly selective channel estimators.