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An Empirical Random-Cluster Model for Subway Channels Based on Passive Measurements in UMTS

机译：基于无源测量的UMTS地铁通道经验随机聚类模型

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摘要

Recently, a measurement campaign for characterizing the channels in underground subway environments was conducted in Shanghai, China. Downlink signals transmitted by 46 universal mobile telecommunication system cells deployed along a 34-km-long subway were collected. Channel impulse responses are extracted from the data received in the common pilot channels, based on which parameters of multipath components are estimated by using a high-resolution parameter algorithm derived using the space-alternating generalized expectation-maximization principle. Multiple time-evolving clusters are obtained, each representing the channel from a remote-radio-unit of a base station to the receiver. Based on a total of 98 time-evolving clusters, channels observed in the station scenario and the tunnel scenario are modeled separately for their distinctive behaviors in many aspects, particularly in the variations of clusters’ trajectories. Intracluster characteristics parameterized by cluster delay and Doppler frequency spreads, $K$ -factor, and dependences among these parameters are investigated. Intercluster parameters, including coexisting cluster number, delay offset, power offset, and cross correlations, are investigated for the station scenario. A path loss model is established for the tunnel scenario.
机译：最近，在中国上海开展了一项测量运动，以表征地下地铁环境中的通道。收集了部署在沿34公里长的地铁上的46个通用移动电信系统单元发送的下行信号。从在公共导频信道中接收的数据中提取信道冲激响应，基于此，通过使用使用空间交替广义期望最大化原理导出的高分辨率参数算法来估计多径分量的参数。获得多个随时间演变的簇，每个簇代表从基站的远端无线电单元到接收机的信道。基于总共98个随时间变化的集群，分别针对车站场景和隧道场景中观察到的通道进行建模，以针对它们在许多方面（尤其是集群轨迹的变化）的独特行为。研究了由群集延迟和多普勒频率扩展，$ K $因子参数化的集群内特性以及这些参数之间的依赖性。针对站方案，研究了群集间参数，包括共存群集数，延迟偏移，功率偏移和互相关。针对隧道场景建立路径损耗模型。

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