A Stochastic Geometry Approach to the Analysis and Optimization of Cellular Networks

Date : 21/03/2020
Catégorie(s) :
Lieu : CentraleSupelec (Gif-sur-Yvette) Amphi F3-06

Monsieur Jian SONG

Soutenance de thèse de doctorat le 19 Décembre 2019, 10h00 à CentraleSupelec (Gif-sur-Yvette) Amphi F3-06

Composition du jury:

M. Marco Di RenzoDirecteur de recherche CNRSDirecteur de Thèse
Mme Maryline HelardProfesseur – IETRPrésident
M. Jalel Ben-OthmanProfesseur-CNRS-CentraleSupélec-Université Paris-Saclay-Université Paris 13Examinateur
M. Jean-Marie GorceProfesseur – INSA-LyonExaminateur
Mme Valeria LoscriChargé de recherche – Inria Lille-Nord EuropeExaminateur
Mme Lina MrouehMaître de conférences – ISEPExaminateur
M. Mustapha BenjillaliMaître de conférences – INPT – MarocRapporteur
M. Laurent ClavierProfesseur – Institut Mines-TelecomRapporteur


The main focus of this thesis is on modeling, performance evaluation and system-level optimization of next-generation cellular networks by using stochastic geometry. In addition, the emerging technology of Reconfigurable Intelligent Surfaces (RISs) is investigated for application to future wireless networks. In particular, relying on a Poisson-based abstraction model for the spatial distribution of nodes and access points, this thesis develops a set of new analytical frameworks for the computation of important performance metrics, such as the coverage probability and potential spectral efficiency, which can be used for system-level analysis and optimization. More specifically, a new analytical methodology for the analysis of three-dimensional cellular networks is introduced and employed for system optimization. A novel resource allocation problem is formulated and solved by jointly combining for the first time stochastic geometry and mixed-integer non-linear programming. The impact of deploying intelligent reflecting surfaces throughout a wireless network is quantified with the aid of line point processes, and the potential benefits of RISs against relaying are investigated with the aid of numerical simulations.

This PhD thesis is supported by the European Commission through the H2020-ETN-5Gaura project under grant 675806.