Coverage and rate analysis for 5G-heterogeneous network: β-Ginibre point process

2021 ◽  
Author(s):  
Mohamed Amine Ouamri
Keyword(s):  
Point Process ◽  
Heterogeneous Network ◽  
Rate Analysis ◽  
Ginibre Point Process

scholarly journals A note on the simulation of the Ginibre point process

2015 ◽  
Vol 52 (4) ◽  
pp. 1003-1012 ◽  
Author(s):  
Laurent Decreusefond ◽  
Ian Flint ◽  
Anais Vergne
Keyword(s):  
Point Process ◽  
Random Matrix ◽  
Point Processes ◽  
Matrix Theory ◽  
Applied Mathematics ◽  
Fixed Number ◽  
Extended Version ◽  
Determinantal Point Processes ◽  
Determinantal Point Process ◽  
Ginibre Point Process

The Ginibre point process (GPP) is one of the main examples of determinantal point processes on the complex plane. It is a recurring distribution of random matrix theory as well as a useful model in applied mathematics. In this paper we briefly overview the usual methods for the simulation of the GPP. Then we introduce a modified version of the GPP which constitutes a determinantal point process more suited for certain applications, and we detail its simulation. This modified GPP has the property of having a fixed number of points and having its support on a compact subset of the plane. See Decreusefond et al. (2013) for an extended version of this paper.

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.

Uplink SINR Analysis in Massive MIMO Systems Using Ginibre Point Process

2019 ◽  
Vol 108 (3) ◽  
pp. 2017-2029
Author(s):  
G. Ananthi ◽  
M. Nithya ◽  
S. J. Thiruvengadam
Keyword(s):  
Point Process ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Ginibre Point Process
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