Definition and Analysis of Quasi-Stationary Intervals of Mobile Radio Channels - Invited Paper

Paetzold, Matthias; Gutiérrez, Carlos

doi:10.1109/vtcspring.2018.8417467

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…Here, the channel non-stationarity is estimated from the time variation of the Doppler spread [47]. The stationary interval is defined as the maximum time duration within which the relative error of Doppler spread beyond a certain threshold, i.e.,…”

Section: Stationary Intervalmentioning

confidence: 99%

A 3D Wideband Non-Stationary Multi-Mobility Model for Vehicle-to-Vehicle MIMO Channels

et al. 2019

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In this paper, a three-dimensional non-stationary multi-mobility vehicle-to-vehicle channel model is proposed. The channel model considers a realistic propagation environment where the transmitter (Tx), receiver (Rx), and scatterers can experience changes in their speeds and moving directions. With different trajectories of the Tx and Rx, the impacts of antenna array rotation are incorporated. The channel impulse response of the proposed multi-mobility channel model is derived using time-varying channel parameters such as angles, delays, and powers. Besides, a simplified two-dimensional non-stationary multi-mobility channel model with approximate expressions is provided. Key statistical properties, e.g., local spatial-temporal cross-correlation function and stationary interval are derived and investigated. The usefulness of the proposed model is validated by comparing the local Doppler power spectrum density and local Doppler spread with the corresponding measurement data. The presented results indicate that the speed and trajectory variations of the Tx and Rx, as well as the motion of scatterers, can have major impacts on channel statistical properties and intensify the channel non-stationarity.INDEX TERMS Non-stationary V2V MIMO channels, GBSM, scatterer motion, trajectory and velocity variations, statistical properties.

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Section: Stationary Intervalmentioning

confidence: 99%

A 3D Wideband Non-Stationary Multi-Mobility Model for Vehicle-to-Vehicle MIMO Channels

et al. 2019

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“…The premise of characterizing the non-stationarity is to determine the size of the region in which the stationary assumption holds. Instead of using the WSS region in which the first-and second-order statistical moments are timeinvariant, we use the term quasi-stationary region (or quasistationarity region) similar to the definitions in [13]- [15]. The quasi-stationary region characterizes the region in which the statistics of channel are similar enough compared to the statistics of the neighboring channel so that the channel can be approximately considered to be stationary [13].…”

Section: Introductionmentioning

confidence: 99%

“…Instead of using the WSS region in which the first-and second-order statistical moments are timeinvariant, we use the term quasi-stationary region (or quasistationarity region) similar to the definitions in [13]- [15]. The quasi-stationary region characterizes the region in which the statistics of channel are similar enough compared to the statistics of the neighboring channel so that the channel can be approximately considered to be stationary [13]. It should be noted that the WSS assumption is stricter than the quasistationary condition according to their definitions.…”

Section: Introductionmentioning

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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“…In particular, wide-sense stationary uncorrelated scattering (WSSUS) models, which are a subset of the category of random LTV systems, are the most common in the literature. As well, more recent work in this field has focused on nonstationary LTV communications channels [5][6][7], of which vehicle-to-vehicle (V2V) communication is a prime example [8]. A separate set of models that are distinct, but can be related to Bello's LTV theory are the Clarke/Jakes Doppler spectrum class of models [9,10], originally only applicable for flat fading (i.e., where the symbol time is much larger than the multipath delay spread).…”

Section: Introductionmentioning

confidence: 99%

Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions

Cooke¹

2019

Entropy

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Bello’s stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense stationary uncorrelated scattering (WSSUS) models. WSSUS models separate the channel from the effect of the waveform and receive filter, making it an ideal formulation for waveform design problems. Of particular interest in the radar waveform design community is the ability to suppress unwanted backscatter from the earth’s surface, known as clutter. Various methods for estimating WSSUS system functions have been studied in the literature, but to date no analytic expressions for radar surface clutter range-Doppler scattering functions exist. In this work we derive a frequency-selective generalization of the Jakes Doppler spectrum model, which is widely used in the wireless communications literature, adapt it for use in radar problems, and show how the maximum entropy method can be used to extend this model to account for internal clutter motion. Validation of the spectral and stationarity properties of the proposed model against a subset of the Australian Ingara sea clutter database is performed, and good agreement is shown.

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Definition and Analysis of Quasi-Stationary Intervals of Mobile Radio Channels - Invited Paper

Cited by 10 publications

References 11 publications

A 3D Wideband Non-Stationary Multi-Mobility Model for Vehicle-to-Vehicle MIMO Channels

A 3D Wideband Non-Stationary Multi-Mobility Model for Vehicle-to-Vehicle MIMO Channels

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

Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions

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