Abstract
This paper investigates the characteristics of the shadowed fading observed in off-body communications channels at 5.8 GHz. This is realized with the aid of the $\kappa $ – $\mu $ /gamma composite fading model, which assumes that the transmitted signal undergoes $\kappa $ – $\mu $ fading, which is subject to multiplicative shadowing. Based on this, the total power of the multipath components, including both the dominant and scattered components, is subject to non-negligible variations that follow the gamma distribution. For this model, we present an integral form of the probability density function (PDF) as well as important analytic expressions for the PDF, cumulative distribution function, moments, and moment generating function. In the case of indoor off-body communications, the corresponding measurements were carried out in the context of four explicit individual scenarios, namely: line of sight (LOS), non-LOS walking, rotational, and random movements. The measurements were repeated within three different indoor environments and considered three different hypothetical body worn node locations. With the aid of these results, the parameters for the $\kappa $ – $\mu $ /gamma composite fading model were estimated and analyzed extensively. Interestingly, for the majority of the indoor environments and movement scenarios, the parameter estimates suggested that dominant signal components existed even when the direct signal path was obscured by the test subject’s body. In addition, it is shown that the $\kappa $ – $\mu $ /gamma composite fading model provides an adequate fit to the fading effects involved in off-body communications channels. Using the Kullback-Leibler divergence, we have also compared our results with another recently proposed shadowed fading model, namely, the $\kappa $ – $\mu $ /lognormal LOS shadowed fading model. It was found that the $\kappa $ – $\mu $ /gamma composite fading model provided a better fit for the majority of the scenarios considered in this paper.
Highlights
I N BODY centric communications, while path loss, largescale and small-scale fading phenomena can be responsible for shaping the characteristics of the received signal, they are often superseded by body shadowing as the predominant fading mechanism
It was found that for all of the indoor environments, irrespective of the hypothetical body worn node locations, path loss exponents for the line of sight (LOS) walking scenario were smaller than those generally anticipated for free space propagation, potentially due to the well-known waveguide effect often experienced within indoor environments
Over all of the measurement scenarios considered in this study, the probability density function (PDF) of the κ–μ/gamma composite fading model has been shown to provide a good fit to the shadowed fading observed in indoor off-body communications channels
Summary
I N BODY centric communications, while path loss, largescale and small-scale fading phenomena can be responsible for shaping the characteristics of the received signal, they are often superseded by body shadowing as the predominant fading mechanism. The authors modeled the channel using a combination of the mean channel gain which was characterized through a linear log-distance fit of the data, the small-scale fading component which was modeled by the Nakagami-m distribution and the body shadowing effect which was considered as an additional loss contribution to the mean channel gain and was described as a function of the body orientation angle This decomposition of the received signal into its shadowed and small-scale fading components is common amongst all the analyses presented in [3] and [6]–[9]. This step may simplify the analysis of the channel data, in some respects it seems an unnatural approach as in reality both shadowed and small-scale fading co-exist and affect body centric communications channels simultaneously Another benefit of using composite fading models to characterize wireless channels is that they circumvent the requirement to choose an appropriate smoothing window size for the computation of the local mean signal.
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