Characterization and Modeling of Temporal Variations on an Ultrawideband Radio Link

Pagani, Pascal; Pajusco, Patrice

doi:10.1109/tap.2006.883961

Cited by 34 publications

(26 citation statements)

References 14 publications

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“…measurement with static TX and RX users. We find that the spectrum is similar in shape to those previously reported for short-range fixed systems [20], [21] which is in line with our intuition; the spectrum is centered around zero Doppler (the Doppler shift of the dominant part). Excluding the contribution at zero Doppler, we find that the fading part of the channel can be well described by a Laplacian Doppler spectrum…”

Section: A Doppler Spectrum and Delay Dispersionsupporting

confidence: 92%

A MIMO channel model for wireless personal area networks

Karedal

Almers

Johansson

et al. 2010

IEEE Trans. Wireless Commun.

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Abstract-Recent years have seen an increasing attention given to wireless Personal Area Networks (PANs), which are typically networks with small transmitter-receiver separation. The desire for high data rates has led to an interest in deploying multipleinput multiple-output (MIMO) transmission for such systems, but up until this date there exists, to the authors' best knowledge, no MIMO channel model that enables performance simulations of such systems. An important characteristic of PANs, and at the same time an important difference to regular wireless local area networks, is the interaction between the antenna array and the user. In conjunction with the irregular antenna arrangements that are typical for PAN devices, this has been shown to lead to flexible channel statistics. In this paper we present a MIMO model for PANs that incorporates these effects by prescribing different small-scale statistics and gains to different antenna elements. The proposed model can thus be seen as a generalization of the classical MIMO model for line-of-sight situations. The model is compared to several sets of measurement data and found to provide a very good description of the essential PAN channel characteristics. We also provide a detailed parameterization of the model for a particular PAN scenario.Index Terms-Personal area networks, channel measurements, MIMO, statistical channel model, irregular antenna arrays.

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Section: A Doppler Spectrum and Delay Dispersionsupporting

confidence: 92%

A MIMO channel model for wireless personal area networks

Karedal

Almers

Johansson

et al. 2010

IEEE Trans. Wireless Commun.

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“…For k → ∞ the Rician fading channel converges to the AWGN channel. Different UWB measurement campaigns show a good fit with the distribution of the path gains with a Rice distribution [20,26,43]. Using (18), the probability density function of the random SNR is:…”

Section: Rician Fadingmentioning

confidence: 99%

MIRA – Physical Layer Optimisation for the Multiband Impulse Radio UWB Architecture

Moorfeld¹,

Finger²,

Dehner³

et al. 2013

Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

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“…direct signal component is mainly affected by obstructing people and is responsible for the large-scale fading observed on the received signal. Based on measurements, Pagani et al [5] proposed a Gaussian-like empirical model for describing the large-scale fading pattern of the signal affected by the human by…”

Section: Model Validationmentioning

confidence: 99%

“…A two-state Ricean model for predicting the indoor wireless channel was reported in [4]. An empirical model for describing the large-scale fading pattern of the signal affected by the human body was presented in [5]. Furthermore, reference [6] presents propagation measurement results in the presence of human activity for a 60 GHz channel.…”

Section: Introductionmentioning

confidence: 99%

Physical-statistical channel model for signal effect by moving human bodies

Cheffena

2012

J Wireless Com Network

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A novel physical-statistical channel model for simulating the signal effect by moving human bodies is presented. The human body is modeled as vertically oriented dielectric cylindrical volume. The received signal is assumed to be composed of a direct component which might be subject to shadowing and a multipath component due to reflection and diffuse scattering, i.e., a Ricean channel. The shadowing effect of the direct signal component is calculated using Kirchhoff diffraction equation. The multipath component is parameterized by calculating the reflected fields from the floor, ceiling and walls of the indoor environment as well as scattered fields from moving human bodies. Poisson and Exponential distributions are used to describe the shadowing and inter-shadowing events caused by multiple bodies, respectively. Furthermore, simulation results of the first and second order statistics of the received signal affected by moving human bodies for 3.35 GHz and 60 GHz signals are presented. In addition, initial validation of the developed model are performed using an empirical model for human body shadowing and reported measurement results.

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Characterization and Modeling of Temporal Variations on an Ultrawideband Radio Link

Cited by 34 publications

References 14 publications

A MIMO channel model for wireless personal area networks

A MIMO channel model for wireless personal area networks

MIRA – Physical Layer Optimisation for the Multiband Impulse Radio UWB Architecture

Physical-statistical channel model for signal effect by moving human bodies

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