Everything you always wanted to know about training: guidelines derived using the affine precoding framework and the CRB
Everything you always wanted to know about training: guidelines derived using the affine precoding framework and the CRB
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DOI：
10.1109/tsp.2005.863031
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发表时间：
2006-03
影响因子：
5.4
通讯作者：
A. Vosoughi;A. Scaglione
中科院分区：
文献类型：
--
作者：
A. Vosoughi;A. Scaglione
本文采用仿射预编码来研究在使用发射机资源进行训练和使用信息符号时存在的权衡。通道输入是一个叠加在线性预编码符号向量上的训练向量。研究了一种块衰落选频多输入多输出（MIMO）信道。为了突出训练和数据符号之间的权衡，在两种情况下推导了Fisher信息矩阵（FIM）：待估计的随机参数向量只包含1)衰落信道系数和2)未知数据符号以及信道系数。策略1对应于初始估计信道并利用信道测量来检索数据符号的接收机结构，策略2对应于联合进行信道和符号估计的结构。本文研究的有趣结果是，在总平均发射功率约束下，最小化策略1和策略2的信道Cramer-Rao界（CRB）导致不同的仿射预编码器设计准则。
In this paper, affine precoding is used to investigate the tradeoffs that exist while using the transmitter resources on training versus information symbols. The channel input is a training vector superimposed on a linearly precoded vector of symbols. A block-fading frequency-selective multi-input multi-output (MIMO) channel is considered. To highlight the tradeoffs between training and data symbols, the Fisher information matrix (FIM) is derived under two circumstances: the random parameter vector to be estimated contains 1) only fading channel coefficients and 2) unknown data symbols as well as the channel coefficients. While strategy 1 corresponds to the receiver structure in which the channel is estimated initially and the channel measurement is utilized to retrieve the data symbols, strategy 2 corresponds to the structure in which channel and symbol estimations are performed jointly. The interesting outcome of the study in this paper is that minimizing the channel Cramer-Rao bound (CRB) for strategies 1 and 2 under a total average transmit power constraint leads to different affine precoder design guidelines.