Shadowing Correlation: Empirical Results for mm-Wave Wireless Links in Urban Street Canyons

Ahumada, L.; Carreno, Erick; Angles, A.; Schkolnik, Demian

doi:10.1109/lawp.2018.2801720

Cited by 16 publications

(10 citation statements)

References 21 publications

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“…We also obtain the characteristic of shadowing by subtracting the path loss modelled in Equation (5) from the measurement data for both measured scenarios. The autocorrelation coefficient of shadowing is obtained by using the method in [28–30]

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ \begin{aligned} r false(normalΔ d false) = \frac{E (}{, (][, χ false(d false) - E ()(, χ false(d false))) false[χ false(d + normalΔ d false) - E false(χ false(d + normalΔ d false) false) false])}{\sqrt{V a r [χ (d)]} \sqrt{V a r [χ (d + Δ d)]}} \end{aligned} \end{matrix}

…”

Section: Measurement Results and Discussionmentioning

confidence: 99%

Distance‐dependent V2I wireless channel characteristics and performance in 5G small cell based on measurements

Wang

et al. 2020

IET Communications

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\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ \begin{aligned} r false(normalΔ d false) = \frac{E (}{, (][, χ false(d false) - E ()(, χ false(d false))) false[χ false(d + normalΔ d false) - E false(χ false(d + normalΔ d false) false) false])}{\sqrt{V a r [χ (d)]} \sqrt{V a r [χ (d + Δ d)]}} \end{aligned} \end{matrix}

…”

Section: Measurement Results and Discussionmentioning

confidence: 99%

Distance‐dependent V2I wireless channel characteristics and performance in 5G small cell based on measurements

Wang

et al. 2020

IET Communications

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“…To find the set of reflecting elements that should be activated, the computation of ( 24) is required for each trial set of reflectors. Therefore, the exhaustive search would require a total of 2 L − 1 times computation of (24). For the SDS, the worst case scenario would require L times computation of (24), which occurs when all κ values are roughly equal for all reflectors.…”

Section: Complexitymentioning

confidence: 99%

BER Reduction Using Partial-Elements Selection in IRS-UAV Communications with Imperfect Phase Compensation

Jangsher¹,

Al‐Jarrah²,

Al-Dweik³

et al. 2022

Preprint

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“…For example, in an urban environment, shadowing caused by obstacles in communication paths can be geographically correlated on a scale from 50 to 200 meters [21]. Moreover, temporal shadow fades with variations below 1 dB (i.e., highly correlated) over an IEEE 802.11ad channel can be commonly measured in urban streets [67]. Fig.…”

Section: ) Shadowingmentioning

confidence: 99%

Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial

Lu¹,

Salehi²,

Haenggi³

et al. 2021

Preprint

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The performance of wireless networks is fundamentally limited by the aggregate interference, which depends on the spatial distributions of the interferers, channel conditions, and user traffic patterns (or queueing dynamics). These factors usually exhibit spatial and temporal correlations and thus make the performance of large-scale networks environment-dependent (i.e., dependent on network topology, locations of the blockages, etc.). The correlation can be exploited in protocol designs (e.g., spectrum-, load-, location-, energy-aware resource allocations) to provide efficient wireless services. For this, accurate system-level performance characterization and evaluation with spatio-temporal correlation are required. In this context, stochastic geometry models and random graph techniques have been used to develop analytical frameworks to capture the spatio-temporal interference correlation in large-scale wireless networks. The objective of this article is to provide a tutorial on the stochastic geometry analysis of large-scale wireless networks that captures the spatio-temporal interference correlation (and hence the signal-to-interference ratio (SIR) correlation). We first discuss the importance of spatiotemporal performance analysis, different parameters affecting the spatio-temporal correlation in the SIR, and the different performance metrics for spatio-temporal analysis. Then we describe the methodologies to characterize spatio-temporal SIR correlations for different network configurations (independent, attractive, repulsive configurations), shadowing scenarios, user locations, queueing behavior, relaying, retransmission, and mobility. We conclude by outlining future research directions in the context of spatiotemporal analysis of emerging wireless communications scenarios.

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Shadowing Correlation: Empirical Results for mm-Wave Wireless Links in Urban Street Canyons

Cited by 16 publications

References 21 publications

Distance‐dependent V2I wireless channel characteristics and performance in 5G small cell based on measurements

Distance‐dependent V2I wireless channel characteristics and performance in 5G small cell based on measurements

BER Reduction Using Partial-Elements Selection in IRS-UAV Communications with Imperfect Phase Compensation

Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial

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