Spatial Characterization for High-Speed Railway Channels Based on Moving Virtual Array Measurement Scheme

Zhou, Tao; Cheng, Tao; Salous, Sana; Liu, Liu

doi:10.1109/lawp.2016.2602402

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…Adopting the virtual array technique offers an additional advantage, i.e., since there is no mutual coupling between neighboring elements, the antenna calibration is not required [53]. The number of equivalent array elements K, is limited by [52] K…”

Section: B Measurement Systemmentioning

confidence: 99%

Non-Stationarity Characterization and Geometry-Cluster-Based Stochastic Model for High-Speed Train Radio Channels

Zhang

Ghazal

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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In time-variant high-speed train (HST) radio channels, the scattering environment changes rapidly with the movement of terminals, leading to a serious deterioration in communication quality. In the system-and link-level simulation of HST channels, this non-stationarity should be characterized and modeled properly. In this paper, the sizes of the quasistationary regions are quantified to measure the significant changes in channel statistics, namely, the average power delay profile (APDP) and correlation matrix distance (CMD), based on a measurement campaign conducted at 2.4 GHz. Furthermore, parameters of the multi-path components (MPCs) are estimated and a novel clustering-tracking-identifying algorithm is designed to separate MPCs into line-of-sight (LOS), periodic reflecting clusters (PRCs) from power supply pillars along the railway, and random scattering clusters (RSCs). Then, a non-stationary geometry-cluster-based stochastic model is proposed for viaduct and hilly terrain scenarios. Furthermore, the proposed model is verified by measured channel statistics such as the Rician K factor and the root mean square delay spread. The temporal autocorrelation function and the spatial cross-correlation function are presented. Quasi-stationary regions of the model are analyzed and compared with the measured data, the standardized IMT-Advanced (IMT-A) channel model, and a published nonstationary IMT-A channel model. The good agreement between the proposed model and the measured data demonstrates the ability of the model to characterize the non-stationary features of propagation environments in HST scenarios.

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Section: B Measurement Systemmentioning

confidence: 99%

Non-Stationarity Characterization and Geometry-Cluster-Based Stochastic Model for High-Speed Train Radio Channels

Zhang

Ghazal

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…Unique HSR scenarios, such as viaduct, cutting, tunnel, station, hilly terrain, rural, and suburban, have a significant impact on propagation characteristics. In our previous work, fading severity and time-frequency-space dispersion of HSR channels in the viaduct and cutting scenarios were deeply characterized based on measurements using Propsound [11][12][13][14][15]. In [15], spatial characteristics, involving angle of arrival (AOA), root-mean-square (RMS) angle spread (AS), and spatial correlation (SC), were analyzed according to a socalled moving virtual antenna array (VAA) scheme.…”

Section: Related Workmentioning

confidence: 99%

“…In our previous work, fading severity and time-frequency-space dispersion of HSR channels in the viaduct and cutting scenarios were deeply characterized based on measurements using Propsound [11][12][13][14][15]. In [15], spatial characteristics, involving angle of arrival (AOA), root-mean-square (RMS) angle spread (AS), and spatial correlation (SC), were analyzed according to a socalled moving virtual antenna array (VAA) scheme. Authors in [16,17] proposed a statistic model for Ricean K-factor, which investigated the impact of the viaduct height and the cutting width.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Nonstationary Characteristics for High-Speed Railway Scenarios

Zhou

Cheng

2018

Wireless Communications and Mobile Computing

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This paper presents the analysis of nonstationary characteristics for high-speed railway (HSR) scenarios, according to passive long-term evolution- (LTE-) based channel measurements. The measurement data collected in three typical scenarios, rural, station, and suburban, are processed to obtain the channel impulse responses (CIRs). Based on the CIRs, the nonstationarity of the HSR channel is studied focusing on the stationarity interval, and a four-state Markov chain model is generated to describe the birth-death process of multipath components. The presented results will be useful in dynamic channel modeling for future HSR mobile communication systems.

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Three-Symbol Spatial Modulation for High Speed Railway Wireless Mobile Communication

Wang

2019

2019 Computing, Communications and IoT Applications (ComComAp)

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Spatial Characterization for High-Speed Railway Channels Based on Moving Virtual Array Measurement Scheme

Cited by 11 publications

References 12 publications

Non-Stationarity Characterization and Geometry-Cluster-Based Stochastic Model for High-Speed Train Radio Channels

Non-Stationarity Characterization and Geometry-Cluster-Based Stochastic Model for High-Speed Train Radio Channels

Analysis of Nonstationary Characteristics for High-Speed Railway Scenarios

Three-Symbol Spatial Modulation for High Speed Railway Wireless Mobile Communication

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