Correlation and Spectral Properties of Vehicle-to-Vehicle Channels in the Presence of Moving Scatterers

Borhani, Alireza; Pätzold, Matthias

doi:10.1109/tvt.2013.2280674

Cited by 72 publications

(52 citation statements)

References 36 publications

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“…We report several other contributions on stochastic models, which mainly account for the temporal second-order statistics of V-V channels [39]- [47]. The most important are the a) temporal correlation function (CF) (or autocorrelation function (ACF)), b) power spectral density (PSD) (or Doppler spectrum), c) level crossing rate (LCR) and d) average fade duration (AFD).…”

Section: Stochastic Channel Modelsmentioning

confidence: 99%

“…References [39]- [45] do not incorporate the realistic impact of other vehicles' mobility, with the latter being treated in [46] and [47]. However, [46] unrealistically considers fixed velocities for all mobile scatterers and [47] unrealistically considers totally random directions of movement of mobile scatterers (i.e., uniformly distributed) 1 and unrealistically equal amplitudes for all diffuse multipath components.…”

Section: Stochastic Channel Modelsmentioning

confidence: 99%

“…However, [46] unrealistically considers fixed velocities for all mobile scatterers and [47] unrealistically considers totally random directions of movement of mobile scatterers (i.e., uniformly distributed) 1 and unrealistically equal amplitudes for all diffuse multipath components. Also [46] and [47] focus on analyzing ACF and PSD only and not LCR and AFD.…”

Section: Stochastic Channel Modelsmentioning

confidence: 99%

“…where (30), we do not consider the directions of movement of mobile scatterers, which cannot be totally random in V-V channels as assumed in [47], but we directly model the AOA and AOD of multipath power with respect to scatterers' motion (see Fig. 4).…”

Section: Ba T R T R T R T R Max T R T R T R T R Bamentioning

confidence: 99%

“…In, [65] the ACF due to scatterers' mobility was determined based on the relevant scattered power. This is different from considering scatterers' velocity distributions (see, e.g., [66] and [67]) and directly applying them to model () S uS pu as in [47]. As demonstrated in [49, Ch.…”

Section: Ba T R T R T R T R Max T R T R T R T R Bamentioning

confidence: 99%

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Generic stochastic modeling of vehicle-to-vehicle wireless channels

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Matolak

2014

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We present a generic statistical characterization of the vehicle-to-vehicle (V-V) wireless channel by adopting a stochastic modeling approach. Our approach is based on the doubly underspread (DU) property of non-wide sense stationary uncorrelated scattering (non-WSSUS) wireless channels, with V-V channels pertaining to this category. DU channels exhibit explicit frequency and time intervals over which they are approximated as WSSUS. We call these intervals restricted time interval (RTI) and restricted bandwidth (RBW), and variations taking place inside them are characterized as small scale variations. Large scale variations take place outside RTI and RBW. In this paper, we focus on small scale variations, thus, our modeling finds its applicability within RTI and RBW. As practical V-V channels exhibit rapid

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Section: Stochastic Channel Modelsmentioning

confidence: 99%

Section: Stochastic Channel Modelsmentioning

confidence: 99%

Section: Stochastic Channel Modelsmentioning

confidence: 99%

Section: Ba T R T R T R T R Max T R T R T R T R Bamentioning

confidence: 99%

Section: Ba T R T R T R T R Max T R T R T R T R Bamentioning

confidence: 99%

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Generic stochastic modeling of vehicle-to-vehicle wireless channels

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2014

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Vehicle‐To‐Vehicle Channels

Bian

Wang

2021

Wiley 5G Ref

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Vehicle‐to‐vehicle (V2V) channel modeling is critical for the design and performance evaluation of intelligent transportation systems and mobile ad hoc networks, which enables vehicles to access various information and therefore improve traffic efficiency and driving safety. This article first reviews the recent V2V channel measurements and discusses important V2V channel characteristics. The differences between V2V channels and conventional cellular channels are clarified. Then, a comprehensive review of V2V channel models, which include two‐dimensional (2D) V2V channel models, three‐dimensional (3D) V2V channel models, 3D nonstationary V2V channel models, general 5G channel models supporting V2V communication scenarios, and multimobility V2V channel model allowing for velocity and trajectory variations, is provided. Furthermore, important statistical properties of the multimobility V2V channel model such as spatial‐temporal cross‐correlation function (ST‐CCF), Doppler power spectrum density (PSD), Doppler spread, and stationary interval are provided. Finally, conclusions and future research directions are summarized.

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Second‐order fading statistics of massive‐MIMO vehicular radio communication channels

Waseer

Nawaz

Gulfam

et al. 2018

Trans Emerging Tel Tech

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This paper presents an intensive study on the fading statistics of massive multiple‐input–multiple‐output vehicular communication channels. A geometry‐based channel model is proposed, where both ends of the link are considered to be equipped with large‐scale three‐dimensional antenna arrays, whereas the scattering volumes in their vicinity are modeled as flexible (ie, shiftable, scalable, and rotatable) along all the dimensions to adapt various dynamic vehicle‐to‐everything radio propagation scenarios. Both ends of the link are modeled as flexible to be static or mobile in different directions and with independent velocities, to adapt both the vehicle‐to‐vehicle and vehicle‐to‐infrastructure scenarios. Joint and marginal three‐dimensional angle‐of‐arrival and angle‐of‐departure statistics of the channel are studied. The analysis is further extended from plain angle‐of‐arrival/angle‐of‐departure statistics to the characterization of dispersion of energy in the angular domain by using various notable angular spread quantifiers. A correlation between the received signals on two different antenna elements of the array, at two different time instants, on two distinct frequencies, and at two different spatial positions along the node mobility route is thoroughly investigated. This analysis is further extended to analytically study the Doppler spectrum characteristics and second‐order fading statistics of the channel. The considered second‐order fading statistics quantifiers are fading rate variance, level crossing rate, average fade duration, and coherence distance. The impact of various physical (eg, link distance, direction, and speed of node mobility), geometric (eg, shape and orientation of scattering volume), and antenna (eg, beam pattern, beam width, and beam switching) parameters on the angular spread, received signal correlations, Doppler spectrum characteristics, and second‐order fading statistics of the channel is thoroughly investigated. The conducted analysis is highly significant for devising efficient interference mitigation methods, error correction codes, modulation schemes, antenna beams, and receiver designs for emerging massive multiple‐input–multiple‐output vehicle‐to‐vehicle communication links, which, in turn, serves the purpose of efficient spectrum utilization.

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Correlation and Spectral Properties of Vehicle-to-Vehicle Channels in the Presence of Moving Scatterers

Cited by 72 publications

References 36 publications

Generic stochastic modeling of vehicle-to-vehicle wireless channels

Generic stochastic modeling of vehicle-to-vehicle wireless channels

Vehicle‐To‐Vehicle Channels

Second‐order fading statistics of massive‐MIMO vehicular radio communication channels

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