Stochastic geometry and random graphs for the analysis and design of wireless networks

By Martin Haenggi, Jeffrey G. Andrews, François Baccelli, Olivier Dousse, Massimo Franceschetti. IEEE Journal on Selected Areas in Communications, 2009

1,761 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/feef4a5af917580055fb7d661fff9d9fe55cb265

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In Mathematical Physics

Citations in the corpus

Enabling Wireless Power Transfer in Cellular Networks: Architecture, Modeling and Deployment (2012, methodology): “Randomly deployed PBs are modeled as a homogeneous PPP similar to the existing models of wireless ad hoc networks (see e.g., [19]).”
Distance distributions in random networks (2012, neutral): “For detailed analysis one could refer to [47] or [48] Section III for short overview.”
Spatio-Temporal Analysis for SINR Coverage in Small Cell Networks (2019, background): “The intrinsic elegance in modeling and analysis has popularized its application in evaluating performance among various wireless systems, including ad-hoc networks [5], cellular networks [9], or more advanced heterogeneous networks [10], even with device-to-device (D2D) communication [11] and multip”
A Tractable Approach to Coverage and Rate in Cellular Networks (2010, background): “The main advantage of this approach is that the base station positions are all independent which allows substantial tools to be brought to bear from stochastic geometry; see [18] for a recent survey that discusses additional related work, in particular [19]–[21].”
Efficiency Resource Allocation for Device-to-Device Underlay Communication Systems: A Reverse Iterative Combinatorial Auction Based Approach (2012, background): “Notice that from stochastic geometry with for Poisson distributions, the users are uniformly located as well if the number of users is known [27].”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, background): “Device-to-Device (D2D) Communications [28], [29], [30], [52], Heterogeneous Networks [31], [32], [33], Vehicular Networks [53], [54] Multiple Input Multiple Output (MIMO) and Massive MIMO [55], [56], [57]”
What should 6G be? (2019, background): “The breakthrough in mathematics would often result in a new research boom in wireless communications, and one example is the stochastic geometry and graph theory applied for wireless network modelling [55].”
Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks (2016, background): “In the randomized topology, the SINR of the mobile user at a random distance can be modeled by a continuous function based on the stochastic geometry theory [163].”
Analytical Evaluation of Fractional Frequency Reuse for OFDMA Cellular Networks (2011, background): “One advantage of this approach is the ability to capture the non-uniform layout of modern cellular deployments due to topographic, demographic, or economic reasons [3], [4], [5].”
Uplink capacity and interference avoidance for two-tier femtocell networks (2007, methodology): “To model the user/hotspot locations, the paper assumes that the macrocell users and femtocell BS are randomly distributed as a Homogeneous Spatial Poisson Point Process (SPPP) (see [14], [15] for background, prior works include [16]–[18]).”
Downlink Cellular Network Analysis With Multi-Slope Path Loss Models (2014, background): “probability can be derived under arbitrary fading regardle ss of the path loss function [23], [24].”
Modeling and Analysis of K-Tier Downlink Heterogeneous Cellular Networks (2011, background): “Importantly, such a model allows useful mathematical tools from stochastic geometry to be brought to bear on the problem [30]–[32], allowing a tractable analytical model that is also accurate.”
Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey (2013, methodology): “It has been applied to ad hoc networks for more than three decades [17], in particular to model and analyze systems with random channel access (e.g., ALOHA [17]–[39] and carrier sensing multiple access (CSMA) [40]–[51]), single- and multitier cellular networks [52]–[80], and networks with cognitive…”
Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial (2016, background): “The theoretical and statistical studies presented in [36]– [38] revealed that cellular networks deviate from the idealized hexagonal grid structure and follows and irregular topology that randomly changes from one geographical location to another.”
Analytical Modeling of Uplink Cellular Networks (2012, background): “The inadequacy of existing approaches has led to an increase d interest in the use of random spatial models for the network topology [12].”
Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come (2019, methodology): “The most suitable analytical tools and spatial models for representing the locations of the transmitters, receivers, and environmental objects in large-scale wireless networks are stochastic geometry and random spatial processes [93–108].”