Moving Aerial Base Station Networks: A Stochastic Geometry Analysis and Design Perspective

Enayati, Saeede; Saeedi, Hamid; Pishro-Nik, Hossein; Yanıkömeroğlu, Halim

doi:10.1109/twc.2019.2907849

Cited by 63 publications

(35 citation statements)

References 33 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

2020

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One of the most promising solutions to the problem of limited flight time of unmanned aerial vehicles (UAVs), is providing the UAVs with power through laser beams emitted from Laser Beam Directors (LBDs) deployed on the ground. In this letter, we study the performance of a laser-powered UAV-enabled communication system using tools from stochastic geometry. We first derive the energy coverage probability, which is defined as the probability of the UAV receiving enough energy to ensure successful operation (hovering and communication). Our results show that to ensure energy coverage, the distance between the UAV and its dedicated LBD must be below a certain threshold, for which we derive an expression as a function of the system parameters. Considering simultaneous information and power transmission through the laser beam using power splitting technique, we also derive the joint energy and the Signal-to-noise Ratio (SNR) coverage probability. The analytical and simulation results reveal some interesting insights. For instance, our results show that we need at least 6 LBDs/10km 2 to ensure a reliable performance in terms of energy coverage probability.Index Terms-Laser-powered UAV, simultaneous wireless information and power transmission, energy coverage, stochastic geometry.

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Section: Introductionmentioning

confidence: 99%

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

2020

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“…There has been recent interest in applying these tools to the analysis of drone networks as well. While the initial studies, e.g., [6], [7], were focused on static drone networks, mobility has also been considered in the more recent works, e.g., [8]- [12]. We discuss these works in more detail next.…”

Section: Introductionmentioning

confidence: 99%

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

Banagar

Dhillon

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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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.

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“…In [7], a superposition of macro and aerial BSs is considered in which probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) propagation models were adopted for the channel. The authors of [8] added mobility to the BPP-modeled DBS network of [6] and designed stochastic trajectory processes for the mobility of DBSs in order to gain the same coverage profile as the static case, while improving the average fade duration. Analysis of the link capacity between drones was performed in [9], where the authors characterized the distance distribution between drones with random 3D trajectories.…”

Section: Introductionmentioning

confidence: 99%

Fundamentals of Drone Cellular Network Analysis under Random Waypoint Mobility Model

Banagar

Dhillon

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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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.

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Moving Aerial Base Station Networks: A Stochastic Geometry Analysis and Design Perspective

Cited by 63 publications

References 33 publications

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

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

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

Fundamentals of Drone Cellular Network Analysis under Random Waypoint Mobility Model

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