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首页> 外文期刊>Wireless Communications Letters, IEEE >A Spatially Consistent Gaussian Process for Dual Mobility in the 3D Space
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A Spatially Consistent Gaussian Process for Dual Mobility in the 3D Space

机译：在3D空间中的双移动性的空间一致的高斯过程

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摘要

Spatially consistent random variables (SCRVs) have been used in many channel models to ensure a smooth time evolution and correlation of the channel coefficients among closely located terminals. However, most of the existing methods which generate SCRVs are restricted to mobility in the two-dimensional (2D) space and those that cover the three-dimensional (3D) space are still limited in terms of accuracy and/or complexity of processing and memory. This letter proposes a sum-of-sinusoids (SoS) method to generate a Gaussian process (GP) described by two different spatial autocorrelation functions (ACFs) considering single and dual mobility in the 3D space. The method was derived analytically for each of the considered ACFs and extends existing methods from the 2D to 3D space. Comparisons with existing baseline solution is carried out showing that the proposed method presents gains in terms of average squared error (ASE) up to 20 dB. Furthemore, the proposed method using only 100 coefficients presented an ASE around 10 dB smaller when compared with the baseline solution using 1000 coefficients.
机译：空间一致的随机变量（SCRV）已被用于许多信道模型中，以确保紧密位终端之间的信道系数的平滑时间演变和相关性。然而，生成SCRV的大多数现有方法仅限于二维（2D）空间中的移动性，并且覆盖三维（3D）空间的步骤仍然是处理和存储器的准确度和/或复杂性的。这封信提出了一种正弦曲线（SOS）方法，用于考虑3D空间中的单个和双移动性，生成由两个不同的空间自相关函数（ACF）描述的高斯过程（GP）。该方法是针对所考虑的ACF中的每一个进行分析导出，并将现有方法从2D扩展到3D空间。进行现有基线解决方案的比较表明该方法在平均平均误差（ASE）上呈现增益，高达20 dB。与使用1000系数的基线解决方案相比，使用仅100系数的所提出的方法呈现了大约10dB的ASE。

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