Path loss models for indoor off-body communications at 60 GHz

Cotton, Simon L.; Chun, Young Jin; Scanlon, William; Conway, Gareth A.

doi:10.1109/aps.2016.7696427

Cited by 6 publications

(2 citation statements)

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“…In [22], an off-body channel at 6-8.5 GHz is studied and a linear path loss model for a hospital environment is proposed, using LSM, with a height-dependent and a body obstruction attenuation factors. One can also find research on the usage of mm-waves for BAN applications, e.g., in [23], where LR is used for elaborating a path loss model for on-body channels at 94 GHz, whereas in [24] and [25] the same statistical tool is used for calculating path loss models for line of sight (LoS) and non-LoS (NLoS) off-body channels at 60 GHz within indoor environments. Although the current article addresses mean path loss modelling methods, it is worthwhile to mention that LR can also be used for modelling the influence of the user's body on channel characteristics, e.g., in [26], for the calculation the parameters of a maximum body-shadowing loss model, which is a component of a more general model for off-and body-tobody communications.…”

Section: Introductionmentioning

confidence: 99%

On the Usefulness of the Generalised Additive Model for Mean Path Loss Estimation in Body Area Networks

et al. 2020

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In this article, the usefulness of the Generalised Additive Model for mean path loss estimation in Body Area Networks is investigated. The research concerns a narrow-band indoor off-body network operating at 2.45 GHz, being based on measurements performed with four different users. The mean path loss is modelled as a sum of four components that depend on path length, antenna orientation angle, absolute difference between transmitting and receiving antenna heights and relative polarisation of both antennas. It is proved that the Generalised Additive Model allows for mean path loss estimation with a higher accuracy in comparison with Linear Regression. The obtained mean error is 0 dB, the root mean square error is 5.52 dB and the adjusted coefficient of determination is 61.2%.

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Section: Introductionmentioning

confidence: 99%

On the Usefulness of the Generalised Additive Model for Mean Path Loss Estimation in Body Area Networks

et al. 2020

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“…Recently, wide range of measurement campaigns had been conducted by many research organizations and consortium to statistically characterize the channel model of 5G communications, including METIS2020 [6], COST2100 [7], [8], NYU WIRELESS [9]- [13] and QUB [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

A Generalized Fading Model with Multiple Specular Components

Chun

2018

Preprint

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The wireless channel of 5G communications will have unique characteristics that can not be fully apprehended by the traditional fading models. For instance, the wireless channel may often be dominated by finite number of specular components, the conventional Gaussian assumption may not be applied to the diffuse scattered waves and the point scatterers may be inhomogeneously distributed.These physical attributes were incorporated into the state-of-the-art fading models, such as the κ-µ shadowed fading model, the generalized two-ray fading model and the fluctuating two ray fading model.Unfortunately, much of the existing published work commonly imposed arbitrary assumptions on the channel parameters to achieve theoretical tractability, thereby limiting their application to represent diverse range of propagation environments. This motivates us to find a more general fading model that incorporates multiple specular components with clusterized diffuse scattered waves, but achieves analytical tractability at the same time. To this end, we introduced the Multiple-Waves with Generalized Diffuse Scatter (MWGD) and Fluctuating Multiple-Ray (FMR) model that allow arbitrary number of specular components and assume generalized diffuse scattered model. We derive the distribution functions of the signal envelop in closed form and calculate second order statistics of the proposed fading model. Furthermore, we evaluate the performance metrics of wireless communications systems, such as the capacity, outage probability and average bit error rate. Through numerical simulations, we obtain important new insights into the link performance of the 5G communications while considering a diverse range of fading conditions and channel characteristics.

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