Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey

By Hesham Elsawy, Ekram Hossain, Martin Haenggi. IEEE Communications Surveys and Tutorials, 2013

974 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bb9a305eca673785523cc91f721b5cd944c0d29e

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Ultra-Dense Networks: A Survey (2016, background): “Stochastic geometry stems as a best-fit tool to study such random network environments [44], [72], [73].”
Cognitive and Energy Harvesting-Based D2D Communication in Cellular Networks: Stochastic Geometry Modeling and Analysis (2014, methodology): “We use a statistical approach based on stochastic geometry [6], [7] to model and evaluate the performance of the proposed system in terms of outage probabilities of D2D users as well as cellular users.”
Analytical Modeling of Mode Selection and Power Control for Underlay D2D Communication in Cellular Networks (2014, background): “Enabling device-to-device (D2D) communication in cellular networks has recently been proposed as a promising solution to improve the spatial frequency reuse and boost up the throughput of cellular networks [1]–[4].”
A Primer on Cellular Network Analysis Using Stochastic Geometry (2016, background): “Please refer to [25], [26] for a more comprehensive summary of the recent results, especially on HCNs.”
Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial (2016, background): “Motivated by its tractability, attempts to promote SG to model cellular networks can be traced back to the late 90’s [34], [35].”
A Survey on Wireless Security: Technical Challenges, Recent Advances, and Future Trends (2015, background): “structure and services have been proliferating with the goal of meeting rapidly increasing demands [1], [2].”
Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols (2016, background): “considered the general principles of CR communications [20], [26]–[30] as well as CR network structures [31]–[37].”
Analysis on Cache-Enabled Wireless Heterogeneous Networks (2015, methodology): “Define the average ergodic rate Ui , i = 1, 2, 3 of the reference user when it accesses to the i-th tier as [6], [7], [24], [25], [27],”
Reconfigurable Intelligent Surfaces: Principles and Opportunities (2020, background): “the homogeneous Poisson point process (HPPP) [47], [48], the Poisson cluster process (PCP) [49], [50], the Binomial point process (BPP) as well as the Hard core point process (HCPP) [51].”
On Stochastic Geometry Modeling of Cellular Uplink Transmission With Truncated Channel Inversion Power Control (2014, methodology): “Therefore, modeling and analysis of cellular networks based on stochastic geometry have recently received much attention due to its tractability and accuracy [3].”
Stochastic Geometry Modeling and Analysis of Multi-Tier Millimeter Wave Cellular Networks (2014, methodology): “Usually, the Poisson Point Process (PPP) is used due to its mathematical tractability [6].”
Software Defined Optical Networks (SDONs): A Comprehensive Survey (2015, background): “For instance, the cell structure of wireless cellular networks [458] has advanced to femtocell networks [459] as well as heterogeneous and multitier cellular structures [460], [461].”