Adaptive MIMO antenna selection via discrete stochastic optimization
Adaptive MIMO antenna selection via discrete stochastic optimization
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DOI：
10.1109/tsp.2005.857056
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发表时间：
2005-11
影响因子：
5.4
通讯作者：
I. Berenguer;Xiaodong Wang;V. Krishnamurthy
中科院分区：
文献类型：
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作者：
I. Berenguer;Xiaodong Wang;V. Krishnamurthy
最近，它已被证明，它是可能的，以提高多输入多输出（MIMO）系统的性能，通过采用更多的天线比实际使用的，并选择最佳的子集的基础上的信道状态信息。现有的天线选择算法假设完美的信道知识和优化标准，如香农容量或各种边界的错误率。本文探讨MIMO天线选择算法的可能的解决方案是大的，只有一个嘈杂的信道估计是可用的。在相同的精神，传统的自适应滤波算法，我们提出了基于模拟的离散随机优化算法，以自适应地选择一个更好的天线子集，使用标准，如最大互信息，错误率的界限等。这些离散随机逼近算法非常适合于最大限度地减少错误率，因为计算的错误率的封闭形式的表达式是棘手的。我们还考虑时变信道的情况下，天线选择算法可以跟踪时变的最佳天线配置。我们提出了几个数值例子来显示这些算法的快速收敛在各种性能标准下，也证明了它们的跟踪能力。
Recently it has been shown that it is possible to improve the performance of multiple-input multiple-output (MIMO) systems by employing a larger number of antennas than actually used and selecting the optimal subset based on the channel state information. Existing antenna selection algorithms assume perfect channel knowledge and optimize criteria such as Shannon capacity or various bounds on error rate. This paper examines MIMO antenna selection algorithms where the set of possible solutions is large and only a noisy estimate of the channel is available. In the same spirit as traditional adaptive filtering algorithms, we propose simulation based discrete stochastic optimization algorithms to adaptively select a better antenna subset using criteria such as maximum mutual information, bounds on error rate, etc. These discrete stochastic approximation algorithms are ideally suited to minimize the error rate since computing a closed form expression for the error rate is intractable. We also consider scenarios of time-varying channels for which the antenna selection algorithms can track the time-varying optimal antenna configuration. We present several numerical examples to show the fast convergence of these algorithms under various performance criteria, and also demonstrate their tracking capabilities.