On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks

Kalantari, Elham; Yanıkömeroğlu, Halim; Yongaçoğlu, Abbas

doi:10.1109/vtcfall.2016.7881122

Cited by 394 publications

(309 citation statements)

References 12 publications

Supporting

Mentioning

281

Contrasting

Unclassified

Order By: Relevance

“…The authors in [3] showed that the UAV-BS placement in the horizontal dimension can be modeled as a circle placement problem and a smallest enclosing circle problem, and they proposed an optimal 3D placement algorithm for maximizing the number of served users with the minimum transmit power. In [4], a method using the heuristic algorithm for finding the positions of UAV-BSs in an area with different user densities was proposed. This algorithm can estimate the minimum number of UAV-BSs and their placement, while satisfying coverage and capacity constraints.…”

Section: Related Workmentioning

confidence: 99%

A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network

Peng

Chen

Lai

et al. 2019

2019 IEEE/CIC International Conference on Communications in China (ICCC)

View full text Add to dashboard Cite

The unmanned aerial vehicles base stations (UAV-BSs) have great potential in being widely used in many dynamic application scenarios. In those scenarios, the movements of served user equipments (UEs) are inevitable, so the UAV-BSs needs to be re-positioned dynamically for providing seamless services. In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs' reposition trajectories. An Echo State Network (ESN) based algorithm for predicting the future trajectories of UEs and a Kuhn-Munkres-based algorithm for finding the energy-efficient reposition trajectories of UAV-BSs is designed, respectively. We conduct a simulation using a real open dataset for performance validation. The simulation results indicate that the proposed framework achieves high prediction accuracy and provides the energy-efficient matching scheme.

show abstract

Section: Related Workmentioning

confidence: 99%

A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network

Peng

Chen

Lai

et al. 2019

2019 IEEE/CIC International Conference on Communications in China (ICCC)

View full text Add to dashboard Cite

show abstract

“…One of these is backhaul connectivity between the UAV-BSs and the core network, which may impact the overall system performance. While various algorithms for optimized UAV-BS deployment have been proposed in recent literature [4], [5], there is a lack of comprehensive studies considering all of the important factors under practical modeling assumptions.…”

Section: Introductionmentioning

confidence: 99%

Aerial Access and Backhaul in mmWave B5G Systems: Performance Dynamics and Optimization

et al. 2020

View full text Add to dashboard Cite

The use of unmanned aerial vehicle (UAV)-based communication in millimeter-wave (mmWave) frequencies to provide on-demand radio access is a promising approach to improve capacity and coverage in beyond-5G (B5G) systems. There are several design aspects to be addressed when optimizing for the deployment of such UAV base stations. As traffic demand of mobile users varies across time and space, dynamic algorithms that correspondingly adjust the UAV locations are essential to maximize performance. In addition to careful tracking of spatiotemporal user/traffic activity, such optimization needs to account for realistic backhaul constraints. In this work, we first review the latest 3GPP activities behind integrated access and backhaul system design, support for UAV base stations, and mmWave radio relaying functionality. We then compare static and mobile UAV-based communication options under practical assumptions on the mmWave system layout, mobility and clusterization of users, antenna array geometry, and dynamic backhauling. We demonstrate that leveraging the UAV mobility to serve moving users may improve the overall system performance even in the presence of backhaul capacity limitations.

show abstract

“…In a dynamic communications network, the link quality between the user and the UAV-BS will vary with the wireless channel, user mobility, communication scenarios, and the location and altitude of the UAV-BS. The key to finding the effective location and height of the UAV-BS with high communications quality is the ability to quickly learn and identify the characteristics of the current 3D wireless channel [1] [2].…”

Section: Introductionmentioning

confidence: 99%

Communications and Networking Technologies for Intelligent Drone Cruisers

Wang

Lai

Shuai

et al. 2019

2019 IEEE Globecom Workshops (GC Wkshps)

View full text Add to dashboard Cite

Future mobile communication networks require an Aerial Base Station (ABS) with fast mobility and long-term hovering capabilities. At present, unmanned aerial vehicles (UAV) or drones do not have long flight times and are mainly used for monitoring, surveillance, and image post-processing. On the other hand, the traditional airship is too large and not easy to take off and land. Therefore, we propose to develop an "Artificial Intelligence (AI) Drone-Cruiser base station that can help 5G mobile communication systems and beyond quickly recover the network after a disaster and handle the instant communications by the flash crowd. The drone-cruiser base station can overcome the communications problem for three types of flash crowds, such as in stadiums, parades, and large plaza so that an appropriate number of aerial base stations can be accurately deployed to meet large and dynamic traffic demands. Artificial intelligence can solve these problems by analyzing the collected data, and then adjust the system parameters in the framework of Self-Organizing Network (SON) to achieve the goals of selfconfiguration, self-optimization, and self-healing. With the help of AI technologies, 5G networks can become more intelligent. This paper aims to provide a new type of service, On-Demand Aerial Base Station as a Service. This work needs to overcome the following five technical challenges: innovative design of drone-cruisers for the long-time hovering, crowd estimation and prediction, rapid 3D wireless channel learning and modeling, 3D placement of aerial base stations and the integration of WiFi front-haul and millimeter wave/WiGig back-haul networks.

show abstract

On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks

Cited by 394 publications

References 12 publications

A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network

A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network

Aerial Access and Backhaul in mmWave B5G Systems: Performance Dynamics and Optimization

Communications and Networking Technologies for Intelligent Drone Cruisers

Contact Info

Product

Resources

About