On-Demand Density-Aware UAV Base Station 3D Placement for Arbitrarily Distributed Users With Guaranteed Data Rates

Lai, Chuan-Chi; Chen, Chun‐Ting; Wang, Li-Chun

doi:10.1109/lwc.2019.2899599

Cited by 126 publications

(75 citation statements)

References 8 publications

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“…The feasibility of supporting UAVs with existing cellular infrastructure is also studied by researchers, where it is concluded that the interference can be minimized by careful design of network parameters to satisfy the quality of service (QoS) requirements [25]. The density-aware UAV placement is studied in [26] for distributed users such that the number of users served by the UAV-based base station is maximized under the given minimum rate requirement per user. Spectrum is another focus area for cellular connected UAVs.…”

Section: Literature Reviewmentioning

confidence: 99%

Unmanned Aerial Vehicles enabled IoT Platform for Disaster Management

et al. 2019

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Efficient and reliable systems are required to detect and monitor disasters such as wildfires as well as to notify the people in the disaster-affected areas. Internet of Things (IoT) is the key paradigm that can address the multitude problems related to disaster management. In addition, an unmanned aerial vehicles (UAVs)-enabled IoT platform connected via cellular network can further enhance the robustness of the disaster management system. The UAV-enabled IoT platform is based on three main research areas: (i) ground IoT network; (ii) communication technologies for ground and aerial connectivity; and (iii) data analytics. In this paper, we provide a holistic view of a UAVs-enabled IoT platform which can provide ubiquitous connectivity to both aerial and ground users in challenging environments such as wildfire management. We then highlight key challenges for the design of an efficient and reliable IoT platform. We detail a case study targeting the design of an efficient ground IoT network that can detect and monitor fire and send notifications to people using named data networking (NDN) architecture. The use of NDN architecture in a sensor network for IoT integrates pull-based communication to enable reliable and efficient message dissemination in the network and to notify the users as soon as possible in case of disastrous situations. The results of the case study show the enormous impact on the performance of IoT platform for wildfire management. Lastly, we draw the conclusion and outline future research directions in this field.

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Section: Literature Reviewmentioning

confidence: 99%

Unmanned Aerial Vehicles enabled IoT Platform for Disaster Management

et al. 2019

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“…Lyu et al [9] investigated the UAV-BS placement problem, and the objective is to minimize the number of required DBSs while each UE is at least covered by one DBS. Lai et al [10] investigated the UAV-BS placement problem in a hot spot area and the goal is to maximize the number of covered UEs under given data rate requirements. Mei et al [11] proposed to utilize cooperative beamforming to alleviate the downlink interference as the DBS reuses the frequency spectra already assigned to the ground BSs, and the target is to maximize the received SINR in the DBS.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Deployment and Throughput of DBSs for Uplink Communications

Zhang

Ansari

2020

IEEE Open J. Veh. Technol.

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Drone-mounted base stations (DBSs) are promising means to provide ubiquitous connections to users and support various emerging applications in mobile networks while full duplex communications has the potential to improve the spectrum efficiency. In this paper, we investigate the backhaul-aware uplink communications in a full-duplex DBS-aided HetNet (BUD) problem with the objective to maximize the total throughput of the network while minimizing the number of deployed DBSs. Since the BUD problem is NP-hard, it is then decomposed into three sub-problems: the joint UE association, power and bandwidth assignment (Joint-UPB) problem, the DBS placement problem (including the vertical and horizontal positions) and the problem of determining the number of DBSs to be deployed. We propose two approximation algorithms to solve the first two sub-problems and use the linear programming to solve the last sub-problem. Finally, another approximation algorithm, referred to as the AA-BUD algorithms, is proposed to solve the BUD problem with guaranteed performance based on the solutions of the three sub-problems. The performance of the AA-BUD algorithm has been demonstrated via extensive simulations, and is shown to be superior to two benchmark algorithms with up to 62% throughput improvement.

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“…Recent study [2] on UAV-BSs mainly focuses on finding the optimal placement of the UAV-BSs while serving the user equipments (UEs) in the target area. However, in most papers [3] [4] [5], few take dynamic scenarios into consideration. In real scenarios, the UAV-BSs are expected to dynamically reposition in response to the dynamic movement of UEs.…”

Section: Introductionmentioning

confidence: 99%

“…1) In the initial time slot, cluster the UEs by using the Kmeans algorithm, and divide UEs into n clusters. 2) Use the density-aware placement algorithm[5] to find the local optimal positions of each cluster to deploy UAV-BSs in the initial time slot. Let the UAV-BSs fly to those positions.…”

mentioning

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)

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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.

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On-Demand Density-Aware UAV Base Station 3D Placement for Arbitrarily Distributed Users With Guaranteed Data Rates

Cited by 126 publications

References 8 publications

Unmanned Aerial Vehicles enabled IoT Platform for Disaster Management

Unmanned Aerial Vehicles enabled IoT Platform for Disaster Management

Optimizing the Deployment and Throughput of DBSs for Uplink Communications

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

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