On distances in uniformly random networks

By Martin Haenggi. IEEE Transactions on Information Theory, 2005

605 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/41fd5e7dcd34c8b05386d84f3abb266cda808c1a

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In Computer Networks and Communications

Citations in the corpus

The Effect of Fading, Channel Inversion, and Threshold Scheduling on Ad Hoc Networks (2007, background): “Translating a Poisson shot-noise point process on onto is discussed in [14] and [36]; in essence, the path-loss exponent changes from to and the intensity increases from to .”
Stochastic geometry and random graphs for the analysis and design of wireless networks (2009, background): “…is the inverse Gaussian or L´evy distribution, as has been established in [51 Using the distribution of the distances to the n -th nearest neighbor [52], the distributions of the interference (without fading) from the n -th nearest neighbor are easily found The tail probabilities do not depend on…”
Interference in Large Wireless Networks (2009, methodology): “The n-th nearest-neighbor distributions of the PPP are provided in [19].”
Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey (2013, neutral): “The distribution of distances for the PPP and BPP was derived in [36] and [38] respectively.”
Distance distributions in random networks (2012, neutral): “, M = 1, 3, . . . , (23) 2 Following [10] we will call it a M -dimensional ball.”
Enabling Wireless Power Transfer in Cellular Networks: Architecture, Modeling and Deployment (2012, background): “Note that D measures the shortest distance between a point in the PPP Ψ to a fixed point and have the following probabilitydensity function [33]”
Coexistence of Wi-Fi and heterogeneous small cell networks sharing unlicensed spectrum (2015, background): “d at distance h from the BS) is able to detect the power density Pf from all co-channel transmitters in an unbounded region. Given knowledge of the spatial distribution of co-channel cell deployments [15], the density of co-channel transmissionsλf can be inferred from the Pf measurements [13]: λf ∝ ”
Downlink Cellular Network Analysis With Multi-Slope Path Loss Models (2014, methodology): “The proof of Lemma 12 is strightforward, using the wellknown distance distribution in Poisson networks [27] and so is omitted from the paper.”