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arXiv 1105.0769cs.ITmath.IT
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复值随机向量和信道:熵、发散和容量

Complex-Valued Random Vectors and Channels: Entropy, Divergence, and Capacity

Georg Tauboeck

摘要:

最近的研究表明,通过利用复值信号的循环性/非循环性或倾向性/不适当性,可以实现显著的性能增益。在本文中,我们调查这些属性的影响,重要的信息理论量,如熵,发散,和容量。我们证明了两个最大熵定理,加强先前已知的结果。前一个定理的证明是基于给定复值随机向量的所谓循环模拟。它的引入是支持的特征定理,采用最小Kullback-Leibler发散准则。另一方面,在后一定理的证明中,利用了复值随机向量的二阶结构的结果。此外,我们解决多输入多输出(MIMO)信道的容量。不管信道参数(噪声向量和信道矩阵,如果建模为随机)的具体分布,我们表明,实现容量的输入向量是圆形的MIMO信道(包括相干和非相干的情况下)的广泛。最后,我们研究了一个不适当的和高斯分布的噪声向量的情况。我们计算容量和容量实现的输入向量,并表明,不适当的增加容量,提供互补协方差矩阵被利用。否则,会发生容量损失,对此我们推导出明确的表达式。

英文摘要:

Recent research has demonstrated significant achievable performance gains by exploiting circularity/non-circularity or propeness/improperness of complex-valued signals. In this paper, we investigate the influence of these properties on important information theoretic quantities such as entropy, divergence, and capacity. We prove two maximum entropy theorems that strengthen previously known results. The proof of the former theorem is based on the so-called circular analog of a given complex-valued random vector. Its introduction is supported by a characterization theorem that employs a minimum Kullback-Leibler divergence criterion. In the proof of latter theorem, on the other hand, results about the second-order structure of complex-valued random vectors are exploited. Furthermore, we address the capacity of multiple-input multiple-output (MIMO) channels. Regardless of the specific distribution of the channel parameters (noise vector and channel matrix, if modeled as random), we show that the capacity-achieving input vector is circular for a broad range of MIMO channels (including coherent and noncoherent scenarios). Finally, we investigate the situation of an improper and Gaussian distributed noise vector. We compute both capacity and capacity-achieving input vector and show that improperness increases capacity, provided that the complementary covariance matrix is exploited. Otherwise, a capacity loss occurs, for which we derive an explicit expression.

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