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On the SNR Penalties of Ideal and Non-ideal Subset Diversity Systems

机译:理想和非理想子集分集系统的SNR损失

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Subset diversity (SSD) techniques, which select and combine the signals from a subset of the available diversity branches, are important for practical wireless systems. This paper characterizes the performance loss, or signal-to-noise ratio (SNR) penalty, of one SSD system with respect to another. Both ideal and non-ideal channel estimation are considered, and the analysis is valid for the important case of arbitrary two-dimensional signal constellations. Expressions are given for the asymptotic SNR penalty, for both small and large SNR, for all the comparisons considered. Additionally, we develop bounds and approximations to quantify the performance of one system in terms of another for all SNRs of interest. Furthermore, for some signal constellations, we derive the exact SNR penalty of a non-ideal system with respect to an ideal system, as well as the exact penalty associated with two non-ideal systems with varying degrees of estimation energy. The SNR penalty enables the assessment of system sensitivity to channel estimation energy, combining architecture, and signal constellation.
机译:子集分集(SSD)技术从可用的分集分支的子集中选择并组合信号,对于实际的无线系统非常重要。本文描述了一个SSD系统相对于另一个SSD系统的性能损失或信噪比(SNR)损失。考虑了理想和非理想信道估计,并且该分析对于任意二维信号星座图的重要情况是有效的。对于所有考虑的比较,均针对小SNR和大SNR给出了渐近SNR损失的表达式。此外,我们针对所有感兴趣的SNR开发了边界和近似值,以量化一个系统相对于另一个系统的性能。此外,对于某些信号星座图,我们得出相对于理想系统的非理想系统的精确SNR损失,以及与两个具有变化程度的估计能量的非理想系统相关的精确损失。 SNR损失可评估系统对信道估计能量的敏感性,并结合架构和信号星座图。

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