Several techniques have been proposed to overcome challenges of meeting demands for higher data rates in wireless communication. Space-time diversity method is proposed to exploit spatiotemporal nature of the channel; hence, a comprehensive knowledge of the spatiotemporal properties of a channel is required. In this paper, a measurement-based channel model that considers both delay and angular domains of an indoor corridor channel for 14 GHz, 18 GHz, and 22 GHz is proposed. A nonparametric Gaussian kernel density estimation method is applied for cluster identification for the three frequency bands. This work proposes a spatiotemporal model that conditions the model parameters on the azimuthal spatial domain. The clusters are modeled on the complete azimuth plane and a Gaussian estimation distribution is fitted onto the empirical data plot. Both clusters and multipath components are modeled and results are compared with Saleh-Valenzuela model parameter values. The results show that both clusters and multipath components can be estimated by probability density functions that follow Gaussian and Laplacian fits on the spatial domain for indoor corridor environment, respectively.

中文翻译：

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室内走廊在14 GHz，18 GHz和22 GHz频段的时空统计信道模型

已经提出了几种技术来克服满足无线通信中对更高数据速率的需求的挑战。提出了一种时空分集的方法来利用信道的时空特性。因此，需要对通道的时空特性有全面的了解。本文提出了一种基于测量的信道模型，该模型同时考虑了14 GHz，18 GHz和22 GHz的室内走廊信道的延迟和角域。将非参数高斯核密度估计方法应用于三个频段的集群识别。这项工作提出了一个时空模型，该模型在方位空间域上调节模型参数。在完整的方位平面上对聚类建模，并将高斯估计分布拟合到经验数据图上。对聚类和多径分量都进行了建模，并将结果与​​Saleh-Valenzuela模型参数值进行了比较。结果表明，可以通过概率密度函数来估计聚类和多径分量，这些概率密度函数分别遵循高斯和拉普拉斯拟合在室内走廊环境的空间域上。