Fundamentals of Drone Cellular Network Analysis under Random Waypoint Mobility Model

Abstract

In this paper, we present the first stochastic geometry-based performance analysis of a drone cellular network in which drone base stations (DBSs) are initially distributed based on a Poisson point process (PPP) and move according to a random waypoint (RWP) mobility model. The serving DBS for a typical user equipment (UE) on the ground is selected based on the nearest neighbor association policy. We further assume two service models for the serving DBS: (i) UE independent model (UIM), and (ii) UE dependent model (UDM). All the other DBSs are considered as interfering DBSs for the typical UE. We introduce a simplified RWP (SRWP) mobility model to describe the movement of interfering DBSs and characterize its key distributional properties that are required for our analysis. Building on these results, we analyze the interference field as seen by the typical UE for both the UIM and the UDM using displacement theorem, which forms the basis for characterizing the average rate at the typical UE as a function of time. To the best of our knowledge, this is the first work that analyzes the performance of a mobile drone network in which the drones follow an RWP mobility model on an infinite plane.

Proactive Handover Decision for UAVs with Deep Reinforcement Learning

“…In reality, a UAV trajectory is either predefined or controlled by a remote pilot. However, to implement a trajectory in a UHD environment, the UAV flies from a starting point to a destination using an RWP mobility model [ 18 , 34 ], which requires destination, speed, and direction to be randomly chosen. To implement an RWP, (1) start and destination points of the UAV trajectory are randomly selected in the environment; (2) three intermediate destinations between the starting and destination points are randomly selected, as shown in Figure 4 .…”

“…This model was inspired by UAV studies in the third-generation partnership project (3GPP), which measures time-varying interference field at gUEs through stochastic geometry and uses it to calculate time-varying coverage probability and data rates [ 17 ]. A similar study by the same authors calculated the data rates for gUEs using time-varying interference fields when UAVs moved based on a RWP mobility model [ 18 ]. A system-level analysis of UAV base stations based mobile network is conducted using different mobility models in a finite 3D space, where constraints such as small-scale fading for line-of-sight and non line-of-sight propagation, and multi-antenna operations are taken into account [ 19 ].…”

Handover Management for Drones in Future Mobile Networks—A Survey

“…In 2019 [115,116], the authors conducted research on the fundamental analysis of drone cellular networks under the random waypoint mobility model. The work compared the performance of an ultra-dense millimeter-wave network architecture having the control and UE-plane with that of a previous architecture.…”