The Price of Incoherence on Co-Transmission Under a Stochastic-Geometry Model

2017 ◽  
Vol 16 (4) ◽  
pp. 2651-2663
Author(s):  
Andreas Polydoros ◽  
Spyridon Evangelatos ◽  
Aris L. Moustakas
Keyword(s):  
Stochastic Geometry ◽  
Geometry Model

scholarly journals A Unified Stochastic Geometry Model for MIMO Cellular Networks With Retransmissions

2016 ◽  
Vol 15 (12) ◽  
pp. 8595-8609 ◽  
Author(s):  
Laila Hesham Afify ◽  
Hesham ElSawy ◽  
Tareq Y. Al-Naffouri ◽  
Mohamed-Slim Alouini
Keyword(s):  
Cellular Networks ◽  
Stochastic Geometry ◽  
Geometry Model

scholarly journals Reconfigurable Intelligent Surfaces Aided Multi-Cell NOMA Networks: A Stochastic Geometry Model

2021 ◽  
pp. 1-1
Author(s):  
Chao Zhang ◽  
Wenqiang Yi ◽  
Yuanwei Liu ◽  
Kun Yang ◽  
Zhiguo Ding
Keyword(s):  
Stochastic Geometry ◽  
Geometry Model

scholarly journals A Cellular Network Model with Ginibre Configured Base Stations

2014 ◽  
Vol 46 (3) ◽  
pp. 832-845 ◽  
Author(s):  
Naoto Miyoshi ◽  
Tomoyuki Shirai
Keyword(s):  
Point Process ◽  
Coverage Probability ◽  
Stochastic Geometry ◽  
Point Processes ◽  
Spatial Configuration ◽  
Base Stations ◽  
Determinantal Point Processes ◽  
Geometry Model ◽  
Wireless Nodes ◽  
Ginibre Point Process

Stochastic geometry models for wireless communication networks have recently attracted much attention. This is because the performance of such networks critically depends on the spatial configuration of wireless nodes and the irregularity of the node configuration in a real network can be captured by a spatial point process. However, most analysis of such stochastic geometry models for wireless networks assumes, owing to its tractability, that the wireless nodes are deployed according to homogeneous Poisson point processes. This means that the wireless nodes are located independently of each other and their spatial correlation is ignored. In this work we propose a stochastic geometry model of cellular networks such that the wireless base stations are deployed according to the Ginibre point process. The Ginibre point process is one of the determinantal point processes and accounts for the repulsion between the base stations. For the proposed model, we derive a computable representation for the coverage probability—the probability that the signal-to-interference-plus-noise ratio (SINR) for a mobile user achieves a target threshold. To capture its qualitative property, we further investigate the asymptotics of the coverage probability as the SINR threshold becomes large in a special case. We also present the results of some numerical experiments.

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