Non-Cooperative Aerial Base Station Placement via Stochastic Optimization

Romero, Daniel; Leus, Geert

doi:10.1109/msn48538.2019.00036

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…60 A typical AirBS requires a maximum transmit power of 5 W. AirBS deployment is not a single variable function, rather it depends on multiple factors as pointed in section ''Fifth generation cellular networks.'' Deployment can be done by maximizing coverage, 61 data rates, [62][63][64] energy efficiency, [65][66][67][68][69][70] along with optimal path planning. [71][72][73][74] Communication applications need optimum outcome, so the parameters need to be optimized for 5G, 6G, or other applications.…”

Section: Motivating Applicationsmentioning

confidence: 99%

Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

Rolly

Malarvezhi

Λάγκας

2022

International Journal of Distributed Sensor Networks

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Next generation wireless networks are expected to be greatly supported by unmanned aerial vehicles, which can act as aerial base stations and constitute a promising solution for the exorbitant rise in user demands. This is possible because of unmanned aerial vehicle characteristics such as mobility, flexibility, increased line-of-sight probability, and their ability to access unreachable locations. Extensive research is now widely performed on the deployment, performance analysis, resource management, trajectory optimization, and channel modeling in such networks. This survey article focuses on the different applications and the related algorithms for realizing aerial base stations by thoroughly reviewing each related research area. In a nutshell, this article provides key applications, challenges, and the technology used for the design and analysis of unmanned aerial vehicles as base stations.

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Section: Motivating Applicationsmentioning

confidence: 99%

Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

Rolly

Malarvezhi

Λάγκας

2022

International Journal of Distributed Sensor Networks

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“…It is worth comparing (10) with (7), where the coefficients in the latter equation are given by (9). It can be easily seen that, except for the mean terms in (10), the estimators provided by ( 10) and ( 7) coincide if one sets κ(x, x ) = Cov[p(x), p(x )] and adjusts λ properly. This is a manifestation of the general fact that a reproducing kernel can be thought of as a generalization of covariance.…”

Section: A Estimation Of Power Mapsmentioning

confidence: 99%

Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography

Romero,

Kim

2022

Preprint

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Radio maps can be utilized to characterize a parameter of interest in a communication channel, such as the received signal strength, at every point of a certain geographical region. This article presents an introductory tutorial to radio map estimation, where radio maps are constructed using spatially distributed measurements. After describing the applications of this kind of maps, this article delves into estimation approaches. Starting by simple regression techniques, gradually more sophisticated algorithms are introduced until reaching state-of-the-art estimators. The presentation of this versatile toolkit is accompanied with toy examples to build up intuition and gain insight into the foundations of radio map estimation. As a secondary objective, this article attempts to reconcile the sometimes conflicting terminology in the literature and to connect multiple bodies of literature and sub-communities that have been working separately in this context.

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“…These difficulties can be sidestepped by resorting to the framework of stochastic optimization. Specifically, adaptive placement for multiple ABSs in 2D space is studied in [15]. There, multiple ABSs adapt their locations by moving in the direction of a gradient estimate of a suitably designed utility function.…”

Section: Adaptive Placementmentioning

confidence: 99%

Aerial Base Station Placement: A Tutorial Introduction

Viet¹,

Romero²

2021

Preprint

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The deployment of Aerial Base Stations (ABSs) mounted on board Unmanned Aerial Vehicles (UAVs) is emerging as a promising technology to provide connectivity in areas where terrestrial infrastructure is insufficient or absent. This may occur for example in remote areas, large events, emergency situations, or areas affected by a natural disaster such as a wildfire or a tsunami. To successfully materialize this goal, it is required that ABSs are placed at locations in 3D space that ensure a high Quality of Service (QoS) to the ground terminals. This paper provides a tutorial introduction to this ABS placement problem where the fundamental challenges and trade-offs are first investigated by means of a toy application example. Next, the different approaches in the literature to address the aforementioned challenges in both 2D or 3D space will be introduced and a discussion on adaptive placement will be provided. The paper is concluded by discussing future research directions.

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Non-Cooperative Aerial Base Station Placement via Stochastic Optimization

Cited by 16 publications

References 17 publications

Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography

Aerial Base Station Placement: A Tutorial Introduction

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