A Novel Non-Stationary Channel Model Utilizing Brownian Random Paths

Borhani, Alireza; Pätzold, Matthias

doi:10.21553/rev-jec.69

Cited by 2 publications

(6 citation statements)

References 23 publications

(46 reference statements)

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“…In reality, however, it has been shown in many experimental studies that this assumption is usually not fulfilled [38]- [40]. In addition, it has been demonstrated in [25] that non-stationarity in time contradicts the isotropic scattering assumption. Motivated by these facts, we extend the above analysis in the next section to non-isotropic scattering.…”

Section: E Pdf Of the Envelopementioning

confidence: 99%

“…. (25) In case that only the transmitter T x or the receiver R x is allowed to change its angle of motion over the time, i.e., b i 0 0 and b l 0 = 0 with {i, l} ∈ {T, R} and i l, the instantaneous phase ϕ mn (t) can be obtained by calculating the limit b l 0 → 0 of (24) as…”

Section: The Reference Modelmentioning

confidence: 99%

“…In [24], a non-stationary nonisotropic F2M channel model has been analysed under the assumption that the MS follows a semi-random movement which has been modelled by a Brownian motion process with drift. It is worth mentioning that the study reported in [25] has demonstrated that the non-stationarity assumption contradicts the commonly made assumption of isotropic scattering. Therefore, the modelling of more realistic M2M channels calls for a combination of non-stationarity aspects and non-isotropic scattering effects.…”

Section: Introductionmentioning

confidence: 96%

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A Non-Stationary Mobile-to-Mobile Channel Model Allowing for Velocity and Trajectory Variations of the Mobile Stations

Dahech

Pätzold

Gutierrez

et al. 2017

IEEE Trans. Wireless Commun.

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In mobile-to-mobile (M2M) communication systems, both the transmitter and the receiver are moving with a certain velocity which is usually assumed to be constant over time. However, in realistic propagation scenarios, the velocity of the mobile stations (MSs) is subject to changes resulting in a nonstationary fading process. In this paper, we develop a nonstationary narrowband M2M multipath fading channel model, where the transmitter and the receiver experience changes in their velocities and trajectories. For this model, we derive expressions for the local autocorrelation function (ACF), the Wigner-Ville spectrum, the local average Doppler shift, and the local Doppler spread under isotropic scattering conditions. In addition, we investigate the correlation properties of the proposed model assuming non-isotropic scattering around the MSs. By relaxing the standard assumption of constant velocities of the MSs, our study shows that the local ACF and the Wigner-Ville spectrum differ completely from known expressions derived for wide-sense stationary (WSS) M2M channel models. Furthermore, it is shown that our model provides consistent results with respect to the Doppler spread. The proposed channel model is useful for the performance analysis of M2M communication systems under non-stationary conditions caused by velocity variations of the MSs.

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Section: E Pdf Of the Envelopementioning

confidence: 99%

Section: The Reference Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

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A Non-Stationary Mobile-to-Mobile Channel Model Allowing for Velocity and Trajectory Variations of the Mobile Stations

Dahech

Pätzold

Gutierrez

et al. 2017

IEEE Trans. Wireless Commun.

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“…This is in contrast with real-world moving scenarios in which the AOM varies along the travelling path. The nonstationary channel model proposed in [8] mitigates this contradiction by assuming that the AOM of the MS changes in time. The geometry-based non-stationary channel models proposed in [9] and [10] capture and analyze the effect moving scatterers, while this effect is automatically captured in the measurement-based non-stationary channel models developed in [4] and [11].…”

Section: Introductionmentioning

confidence: 99%

“…A similar approach to the development of non-stationary channel models is to update the 2 channel characteristics along the travelling path of the MS, which is surrounded by randomly distributed scatterers. For instance, the authors in [8], [15] provide a non-stationary one-ring channel model under the assumption that the MS is moving along a fixed trajectory surrounded by a ring of randomly distributed scatterers. In this paper, this approach is featured by allowing the trajectory of the MS to be random, while the position of the scatterers is fixed.…”

Section: Introductionmentioning

confidence: 99%

A Random Trajectory Approach for the Development of Non-Stationary Channel Models Capturing Different Scales of Fading

Borhani

Stuber

Pätzold

2016

IEEE Trans. Veh. Technol.

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This paper introduces a new approach to develop stochastic non-stationary channel models, the randomness of which originates from a random trajectory of the mobile station (MS), rather than from the scattering area. The new approach is employed by utilizing a random trajectory model based on the primitives of Brownian fields (BFs), while the position of scatterers can be generated from an arbitrarily two-dimensional (2D) distribution function. The employed trajectory model generates random paths, along which the MS travels from a given starting point to a fixed predefined destination point.To capture the path loss, the gain of each multipath component is modelled by a negative power law applied to the travelling distance of the corresponding plane wave, while the randomness of the path travelled results in large-scale fading. It is shown that the local received power is well approximated by a Gaussian process in logarithmic scale even for a very limited number of scatterers. It is also shown that the envelope of the complex channel gain follows closely a Suzuki process, indicating that the proposed channel model superimposes small-scale fading and large-scale fading. The local power delay profile (PDP) and the local Doppler power spectral density (PSD) of the channel model are also derived and analyzed.

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A Novel Non-Stationary Channel Model Utilizing Brownian Random Paths

Cited by 2 publications

References 23 publications

A Non-Stationary Mobile-to-Mobile Channel Model Allowing for Velocity and Trajectory Variations of the Mobile Stations

A Non-Stationary Mobile-to-Mobile Channel Model Allowing for Velocity and Trajectory Variations of the Mobile Stations

A Random Trajectory Approach for the Development of Non-Stationary Channel Models Capturing Different Scales of Fading

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