Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial

This paper presents a tutorial on stochastic geometry (SG)-based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. This paper starts by modeling and analyzing the baseband interference in a baseline single-tier downlink cellular network with single antenna base stations and universal frequency reuse. Then, it characterizes signal-to-interference-plus-noise-ratio and its related performance metrics. In particular, a unified approach to conduct error probability, outage probability, and transmission rate analysis is presented. Although the main focus of this paper is on cellular networks, the presented unified approach applies for other types of wireless networks that impose interference protection around receivers. This paper then extends the unified approach to capture cellular network characteristics (e.g., frequency reuse, multiple antenna, power control, etc.). It also presents numerical examples associated with demonstrations and discussions. To this end, this paper highlights the state-of-the-art research and points out future research directions

Drone Mobile Networks: Performance Analysis Under 3D Tractable Mobility Models

Reliable wireless communication networks are a significant but challenging mission for post-disaster areas and hotspots in the era of information. However, with the maturity of unmanned aerial vehicle (UAV) technology, drone mobile networks have attracted considerable attention as a prominent solution for facilitating critical communications. This paper provides a system-level analysis for drone mobile networks on a finite three-dimensional (3D) space. Our aim is to explore the fundamental performance limits of drone mobile networks taking into account practical considerations. Most existing works on mobile drone networks use simplified mobility models (e.g., fixed height), but the movement of the drones in practice is significantly more complicated, which leads to difficulties in analyzing the performance of the drone mobile networks. Hence, to tackle this problem, we propose a stochastic geometry-based framework with a number of different mobility models including a random Brownian motion approach. The proposed framework allows to circumvent the extremely complex reality model and obtain upper and lower performance bounds for drone networks in practice. Also, we explicitly consider certain constraints, such as the small-scale fading characteristics relying on line-of-sight (LOS) and non line-of-sight (NLOS) propagation, and multi-antenna operations. The validity of the mathematical findings is verified via Monte-Carlo (MC) simulations for various network settings. In addition, the results reveal some design guidelines and important trends for the practical deployment of drone networks