On the Stationarity Time of a Vehicle-to-Infrastructure Massive Radio Channel in a Line-of-Sight Suburban Environment

Author:

Dahmouni Nor El IslamORCID,Laly PierreORCID,Yusuf MarwanORCID,Delbarre GauthierORCID,Liénard MartineORCID,Simon Eric P.ORCID,Gaillot Davy P.ORCID

Abstract

Massive multiple-input multiple-output (mMIMO) communication systems are a pillar technology for 5G. However, the wireless radio channel models relying on the assumption of wide-sense stationary uncorrelated scattering (WSSUS) may not always be valid for dynamic scenarios. Nonetheless, an analysis of the stationarity time that validates this hypothesis for mMIMO vehicular channels as well as a clear relationship with the scattering properties is missing in the literature. Here, time-varying single-user mMIMO radio channels were measured in a suburban environment at the 5.89 GHz vehicular band with a strong Line-of-Sight (LOS) to study the non-WSSUS and large scale characteristics of the vehicle-to-infrastructure (V2I) link. The generalized local scattering function (GLSF), computed from the sampled channels, was used to derive (1) the spatial distribution of the stationarity time using the channel correlation function (CCF) and empirical collinearity methods and (2) the root mean square delay/angular spread and coherence time/bandwidth values from the projected power delay profile (PDP) and Doppler power spectra (DPS). The results highlight the high degree of correlation between the spatial distribution of the stationarity time and the scattering properties along the measurement route.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimizing Communication in High-Speed Train Environments over 5G Signals through MIMO Base Station Positioning;2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI);2024-06-21

2. Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments;Sensors;2023-01-28

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