Statistical channel model based on α-stable random processes and application to the 60 GHz ultra wide band channel

Azzaoui, Nourddine; Clavier, Laurent

doi:10.1109/tcomm.2010.05.090069

Cited by 37 publications

(32 citation statements)

References 42 publications

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“…Furthermore, the measurements conducted in [205] were used for modeling the path-loss for an indoor scenario associated both with LOS and NLOS environments for transmission over a 60 GHz channel using both omni-directional as well as directional transmit and receive antennas. Azzaoui and Clavier introduced in [206] a statistical channel model for a 2 GHz wide channel.…”

Section: A Mmwave Channel Modeling Effortsmentioning

confidence: 99%

“…An accurate wideband channel model requires numerous parameters for characterizing the channel, whilst the channel model described in (21) is a narrow-band one, which only describes the spatial characteristics of the mmWave channel. In order to extend this channel model to a wideband model, its delay-profile also has to be characterized [7], [45], [125], [176], [206]. For instance, instead of being exclusively specified in terms of θ, ϕ, t and the angular spread shown in Figure 23, the channel impulse response should be extended to include the temporal characteristics of the channel represented by the delay τ .…”

Section: ) Indoor Channel Modelsmentioning

confidence: 99%

“…The spatio-temporal characteristics of the 60 GHz channel were also studied in [14], [28], [66], [69], [120], [121], [125], [142], [166], [175], [206], where the model of the indoor 60 GHz channel is based on the TSV [67] and on the extended-SV [171], [172] channel models. The general double-directional wideband CIR is given by:…”

Section: As Ds θmentioning

confidence: 99%

See 2 more Smart Citations

Millimeter-Wave Communications: Physical Channel Models, Design Considerations, Antenna Constructions, and Link-Budget

Hemadeh

Satyanarayana

El‐Hajjar

et al. 2018

IEEE Commun. Surv. Tutorials

554

393

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Abstract-The millimeter wave (mmWave) frequency band spanning from 30 GHz to 300 GHz constitutes a substantial portion of the unused frequency spectrum, which is an important resource for future wireless communication systems in order to fulfill the escalating capacity demand. Given the improvements in integrated components and enhanced power efficiency at high frequencies, wireless systems can operate in the mmWave frequency band. In this paper, we present a survey of the mmWave propagation characteristics, channel modeling and design guidelines, such as system and antenna design considerations for mmWave, including the link budget of the network, which are essential for mmWave communication systems. We commence by introducing the main channel propagation characteristics of mmWaves followed by channel modeling and design guidelines. Then, we report on the main measurement and modeling campaigns conducted in order to understand the mmWave band's properties and present the associated channel models. We survey the different channel models focusing on the channel models available for the 28 GHz, 38 GHz, 60 GHz and 73 GHz frequency bands. Finally, we present the mmWave channel model and its challenges in the context of mmWave communication systems design.

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Section: A Mmwave Channel Modeling Effortsmentioning

confidence: 99%

Section: ) Indoor Channel Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Millimeter-Wave Communications: Physical Channel Models, Design Considerations, Antenna Constructions, and Link-Budget

Hemadeh

Satyanarayana

El‐Hajjar

et al. 2018

IEEE Commun. Surv. Tutorials

554

393

View full text Add to dashboard Cite

show abstract

“…The first class is based on measured data and only is feasible for the environment from which it has been inferred (Karedal et al, 2009;3GPP TR, 2012). The second class is based on random processes, and the MIMO channel matrix is modeled by sub-channels that are independent and identically distributed random variables (Azzaoui & Clavier, 2010;Jeong et al, 2013). These models cannot consider the characteristics of buildings; for example, the effects of the material buildings are ignored.…”

Section: Introductionmentioning

confidence: 99%

A New Multiple-Input–Multiple-Output Uniform Theory of Diffraction Based Channel Model for Multiple Building Diffraction in Urban Environments

et al. 2014

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This article introduces a new channel model in urban environments that considers multiple antennas in both the transmitter and receiver; moreover, the uniform theory of diffraction is used for modeling rays that impinge on buildings. Against prior models, this model calculates sub-channels of the channel matrix rigorously and does not use any random variables or distribution functions. This model can predict channel and correlation matrixes and channel capacity in a multiple-inputmultiple-output system. Furthermore, the 3GPP spatial channel measurement-based model, which is statistical, is compared with the simulation results of this study. The comparison between the two models shows that the proposed model accuracy is enough for further design of different aspects of the multiple-input-multiple-output system. Therefore, the proposed model can successfully be used to improve theoretical methods for predicting a more realistic multiple-input-multiple-output channel matrix in cellular mobile communications, where diffraction from a number of buildings has to be considered.

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“…Further investigations is needed to validate this approach. We also assume that the channel is accurately represented by an α-stable model as we proposed in [2].…”

Section: Introductionmentioning

confidence: 99%

UWB channel modeling for objects evolving in impulsive environnements

Azzaoui

Clavier

2012

2012 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

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We consider channel modeling issues in the context where communicating objects are evolving in impulsive environments. It was shown recently that α-stable random processes are attractive solution for representing the ultra wide band communication channel in relatively large spatial areas. In this paper, we consider the α-stable channel modeling in an evolutionary context where the model features depend on spatial locations. We introduce a methodological approach consisting of two parametric and non parametric components: the latter can be seen as black box model to describe the spatial evolution and it can be learned from historical observations of the transfer function. The other component concerns the frequency dependence and has an auto-regressive structure.

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Statistical channel model based on α-stable random processes and application to the 60 GHz ultra wide band channel

Cited by 37 publications

References 42 publications

Millimeter-Wave Communications: Physical Channel Models, Design Considerations, Antenna Constructions, and Link-Budget

Millimeter-Wave Communications: Physical Channel Models, Design Considerations, Antenna Constructions, and Link-Budget

A New Multiple-Input–Multiple-Output Uniform Theory of Diffraction Based Channel Model for Multiple Building Diffraction in Urban Environments

UWB channel modeling for objects evolving in impulsive environnements

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