Stochastic Geometry and Wireless Networks, Volume 1: Theory

By François Baccelli, B. Błaszczyszyn. 2009

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Citations in the corpus

Scheduling Policies for Federated Learning in Wireless Networks (2019, methodology): “By Slivnyark’s theorem [33], the interfering points form a PPP with spatial density λG and the transmission success probability can be calculated as”
Cognitive and Energy Harvesting-Based D2D Communication in Cellular Networks: Stochastic Geometry Modeling and Analysis (2014, methodology): “According to Slivnyak’s theorem [6], these results should be then valid for any generic user.”
Analysis of Blockage Effects on Urban Cellular Networks (2013, background): “Since the Poisson law is preserved by independent thinning [20], Φ( , w, θ) is a PPP.”
Coverage and Rate Analysis for Millimeter-Wave Cellular Networks (2014, methodology): “ρ= 2πλ R∞ 0 p(t)tdt. Proof: The average number of LOS base stations can be computed as ρ= E " X Xℓ∈Φ I(Xℓ∈ ΦL) # (a) = 2πλ Z∞ 0 p(t)tdt, where (a) follows directly from Compbell’s formula of PPP [26]. A direct corollary of Theorem 4 follows as below. Corollary 4.1: When p(x) = SR(x), the averag”
Tractable Model for Rate in Self-Backhauled Millimeter Wave Cellular Networks (2014, background): “re most attractive. In each simulation trial, users and BSs are dropped randomly in these geographical areas as per the corresponding densities. 3Notion of typicality is enabled by Slivnyak’s theorem [34]. Users are dropped only in the outdoor regions, whereas the BSs landing inside a building polyg”
Spatio-Temporal Analysis for SINR Coverage in Small Cell Networks (2019, unknown): “By applying Slivnyark’s theorem [7] to the stationary PPP of SAP, it is sufficient to focus on the SINR of a typical UE at the origin, with its tagged SAP located at x0.”
Model-Based Multiple Instance Learning (2017, unknown): “, [12], [18], [26], [27] and references therein.”
Energy Efficiency in Wireless Networks via Fractional Programming Theory (2015, background): “However, in this case, additional mathematical tools like random matrix theory (RMT) [165, 47] and stochastic geometry (SG) [11, 175] are required to statistically characterize the network interference levels.”
Modeling heterogeneous network interference (2012, methodology): “Other non-singular models and tradeoffs between different models are discussed in [8], [23].”