Correlated link shadow fading in multi-hop wireless networks

Abstract

Accurate representation of the physical layer is required for analysis and simulation of multi-hop networking in sensor, ad hoc, and mesh networks. This paper investigates, models, and analyzes the correlations that exist in shadow fading between links in multi-hop networks. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated fading. We describe a measurement procedure and campaign to measure a large number of multi-hop networks in an ensemble of environments. The measurements show statistically significant correlations among shadowing experienced on different links in the network, with correlation coefficients up to 0.33. We propose a statistical model for the shadowing correlation between link pairs which shows strong agreement with the measurements, and we compare the new model with an existing shadowing correlation model of Gudmundson (1991). Finally, we analyze multi-hop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater.

On the Feasibility of Wireless Shadowing Correlation Models

“…Shadowing correlation can occur in various particular scenarios, specifically in peer-to-peer links [2], [67], [68], in indoor, cross-floor, and indoor-outdoor links [27], [30], and in satellite-ground communications [31], [69]- [71]. It should be noted however that most of the literature on correlation in shadowing is driven by cellular (usually urban) scenarios, where the base station/mobile and uplink/downlink dualities apply.…”

“…Three algorithms for doing so are the sum-ofsinusoids (SoS) algorithm [67], the network shadowing (NeSh) method [2], [3], [44], [68], and the over-obstacle multiple-edge diffraction model [75].…”

Radio Tomographic Imaging for Ambient Assisted Living

“…Each element x n of x represents the change in the propagation field caused by the presence of a person in voxel n. The linear model for the change in the propagation field is based on the correlated shadowing models introduced in [11,27] and the work in [12]. The change in RSS caused by the presence of people can be quantified using different methods.…”

“…In RTI, the change in RSS measured on a link is assumed to be a spatial integral of the radio propagation field in the monitored area [11,27]. Due to this, some voxels of the discretized image affect the RSS of a specific link, while some others do not.…”

Appendix E: Radio Signal Propagation Model

“…The small-scale fading, F a (k, s), is usually modeled as lognormal distribution and can be neglected for simplicity. The shadow fading, S a (k, s), for a propagation link from the RF transmitter to the ath cell can be modeled as [1][2][3] follows: …”