Robust Training Sequence Design for Correlated MIMO Channel Estimation
Robust Training Sequence Design for Correlated MIMO Channel Estimation
复制标题
相关 MIMO 信道估计的鲁棒训练序列设计
DOI：
10.1109/acssc.2012.6489055
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发表时间：
2012-11
影响因子：
5.4
通讯作者：
Bengtsson, Mats
中科院分区：
文献类型：
--
作者：
Shariati, Nafiseh;Wang, Jiaheng;Bengtsson, Mats
本文研究了如何设计一个用于多输入多输出（MIMO）信道估计的最坏情况鲁棒训练序列。我们将信道估计的均方误差视为品质因数，其是MIMO信道的二阶统计的函数，即，信道协方差矩阵，以便在总功率约束下优化训练序列。在实际应用中，信道协方差矩阵并不是完全已知的。因此，我们设计的主要方面是提高训练序列对可用信道协方差矩阵中可能的不确定性的鲁棒性。使用一个确定性的不确定性模型，我们制定了一个强大的训练序列设计作为一个极大极小优化问题，我们考虑到这样的不完善。我们研究的鲁棒设计问题，假设一般情况下的任意相关的MIMO信道和非空紧凸不确定集。我们证明了这样的问题承认一个全局最优解，利用凹凸结构的目标函数，并提出数值算法来解决强大的训练设计问题。我们进行分析，考虑多输入单输出（MISO）信道和克罗内克结构MIMO信道沿着与酉不变的不确定性集。对于这些情况下，我们表明，问题是对角化的标称协方差矩阵的特征向量，使强大的设计显着简化，从一个复杂的矩阵变量的问题，一个真实的向量变量的功率分配问题。对于MISO信道，我们提供了由谱范数和核范数定义的不确定集的鲁棒训练序列的封闭形式的解决方案。
We study how to design a worst-case robust training sequence for multiple-input multiple-output (MIMO) channel estimation. We consider mean-squared error of channel estimates as the figure of merit which is a function of second-order statistics of the MIMO channel, i.e., channel covariance matrix, in order to optimize training sequences under a total power constraint. In practical applications, the channel covariance matrix is not known perfectly. Thus the main aspect of our design is to improve robustness of the training sequences against possible uncertainties in the available channel covariance matrix. Using a deterministic uncertainty model, we formulate a robust training sequence design as a minimax optimization problem where we take such imperfections into account. We investigate the robust design problem assuming the general case of an arbitrarily correlated MIMO channel and a non-empty compact convex uncertainty set. We prove that such a problem admits a globally optimal solution by exploiting the convex-concave structure of the objective function, and propose numerical algorithms to address the robust training design problem. We proceed the analysis by considering multiple-input single-output (MISO) channels and Kronecker structured MIMO channels along with unitarily-invariant uncertainty sets. For these scenarios, we show that the problem is diagonalized by the eigenvectors of the nominal covariance matrices so that the robust design is significantly simplified from a complex matrix-variable problem to a real vector-variable power allocation problem. For the MISO channel, we provide closed-form solutions for the robust training sequences with the uncertainty sets defined by the spectral norm and nuclear norm.
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影响因子：
5.4
作者：
Pascual-Iserte, A;Palomar, DP;Lagunas, MA
通讯作者：
Lagunas, MA
影响因子：
2.5
作者：
S. Kay
通讯作者：
S. Kay
影响因子：
2.5
作者：
Yonina C. Eldar
通讯作者：
Yonina C. Eldar
DOI：
10.1109/glocom.2013.6831597
发表时间：
2013-06
期刊：
2013 IEEE Global Communications Conference (GLOBECOM)
影响因子：
--
作者：
Jiaheng Wang;M. Bengtsson;B. Ottersten;D. Palomar
通讯作者：
Jiaheng Wang;M. Bengtsson;B. Ottersten;D. Palomar
影响因子：
8.3
作者：
Shiu, DS;Foschini, GJ;Kahn, JM
通讯作者：
Kahn, JM