3GPP-Inspired Stochastic Geometry-Based Mobility Model for a Drone Cellular Network

Abstract

This paper deals with the stochastic geometry-based characterization of the time-varying performance of a drone cellular network in which the initial locations of drone base stations (DBSs) are modeled as a Poisson point process (PPP) and each DBS is assumed to move on a straight line in a random direction. This drone placement and trajectory model closely emulates the one used by the third generation partnership project (3GPP) for drone-related studies. Assuming the nearest neighbor association policy for a typical user equipment (UE) on the ground, we consider two models for the mobility of the serving DBS: (i) UE independent model, and (ii) UE dependent model. Using displacement theorem from stochastic geometry, we characterize the time-varying interference field as seen by the typical UE, using which we derive the time-varying coverage probability and data rate at the typical UE. We also compare our model with more sophisticated mobility models where the DBSs may move in nonlinear trajectories and demonstrate that the coverage probability and rate estimated by our model act as lower bounds to these more general models. To the best of our knowledge, this is the first work to perform a rigorous analysis of the 3GPP-inspired drone mobility model and establish connection between this model and the more general non-linear mobility models.

Proactive Handover Decision for UAVs with Deep Reinforcement Learning

“…To conduct a realistic and practically viable UAV study, it is essential to design UAV mobility models that are close to real mobility patterns. A mobility model for a UAV-mounted base station was studied in [ 17 ], where the initial position of the base station was modeled as a Poisson point process, and each UAV base station moved in a straight line in a random direction. 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 mobility model for a UAV-mounted base station was studied in [ 17 ], where the initial position of the base station was modeled as a Poisson point process, and each UAV base station moved in a straight line in a random direction. 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 ].…”

Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks

“…Most of the related stochastic geometry-based literature aims to study the coverage probability in the following two main scenarios, that involve drones: 1) drone-assisted aerial cellular networks serving ground users; and 2) ground cellular network serving drone users. For example, papers such as [16]- [18], evaluate the cellular coverage performance of drone assisted aerial cellular networks, without considering any realistic A2G channel characteristics and antenna patterns. While the authors in [19], study the impact of a mixed LoS/NLoS A2G channel on the performance of a droneassisted heterogeneous networks, possible impacts of 3D antenna patterns were ignored.…”

Stochastic Geometry-Based Analysis of Airborne Base Stations With Laser-Powered UAVs

“…UAVs' main attractions include flexible deployment, high maneuverability, and the continuous decrease in their cost. These advantages motivated integrating UAV-mounted BSs into existing cellular networks to improve coverage and capacity [2], while taking advantage of the maneuverability of the UAVs to optimize its path [3], [4]. However, the feasibility and the reliability of the UAV-enabled applications still face some crucial challenges, especially for long-duration missions, due to the limited energy resources on-board [1], [5].…”