On-demand deployment of multiple aerial base stations for traffic offloading and network recovery

Sharafeddine, Sanaa; Islambouli, Rania

doi:10.1016/j.comnet.2019.03.016

Cited by 61 publications

(27 citation statements)

References 33 publications

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“…To define the order of preference, one would rely on equations (6) and (11) as they express the objective function to minimize (minimizing the outage probability for each user). However, as it can be seen from those equations, computing the outage probability for a user depends on the termsγ tv (for a UE) or A tv and B tv (for a UAV).…”

Section: Matching Sub-game Optimizationmentioning

confidence: 99%

“…The power optimization procedure will be considered for each sub-carrier of the set of BSs. As it can be noticed form (6) and (11), the outage probability for a user is impacted by the transmission power employed by the serving and interfering BSs. This is referred respectively by the termsγ uv andγ tv in Theorem 1 and by the terms A uv , B uv , A tv and B tv in Theorem 2.…”

Section: Definition 2 Matching Stabilitymentioning

confidence: 99%

“…In order to enhance the link quality, other works focus on the optimal deployment of BS-UAVs. In [11], the authors addressed the on-demand deployment of multiple aerial base stations for traffic offloading/network recovery. The 3D discrete search space problem is formulated as a mixed-integer program, while a UAV deployment algorithm in a continuous 3D space is proposed based on an unsupervised learning technique.…”

Section: Introductionmentioning

confidence: 99%

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Joint Sub-Carrier and Power Allocation for Efficient Communication of Cellular UAVs

Hellaoui

Bagaa

Chelli

et al. 2020

IEEE Trans. Wireless Commun.

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Cellular networks are expected to be the main communication infrastructure to support the expanding applications of Unmanned Aerial Vehicles (UAVs). As these networks are deployed to serve ground User Equipment (UEs), several issues need to be addressed to enhance cellular UAVs' services. In this paper, we propose a realistic communication model on the downlink, and we show that the Quality of Service (QoS) for the users is affected by the number of interfering BSs and the impact they cause. The joint problem of sub-carrier and power allocation is therefore addressed. Given its complexity, which is known to be NP-hard, we introduce a solution based on game theory. First, we argue that separating between UAVs and UEs in terms of the assigned sub-carriers reduces the interference impact on the users. This is materialized through a matching game. Moreover, in order to boost the partition, we propose a coalitional game that considers the outcome of the first one and enables users to change their coalitions and enhance their QoS. Furthermore, a power optimization solution is introduced, which is considered in the two games. Performance evaluations are conducted, and the obtained results demonstrate the effectiveness of the propositions.

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Section: Matching Sub-game Optimizationmentioning

confidence: 99%

Section: Definition 2 Matching Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Sub-Carrier and Power Allocation for Efficient Communication of Cellular UAVs

Hellaoui

Bagaa

Chelli

et al. 2020

IEEE Trans. Wireless Commun.

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show abstract

“…Thus, we proposed an idea to justify further work's reasoning of the initial and final location of drones. The authors in paper [5] propose a discrete and continuous environment to determine the location of drones based on the users. However, they fail to consider where these drones will begin and end their mission.…”

Section: Related Workmentioning

confidence: 99%

Facility Location Problem Approach for Distributed Drones

Lynskey

Thar

et al. 2019

Symmetry

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Currently, industry and academia are undergoing an evolution in developing the next generation of drone applications. Including the development of autonomous drones that can carry out tasks without the assistance of a human operator. In spite of this, there are still problems left unanswered related to the placement of drone take-off, landing and charging areas. Future policies by governments and aviation agencies are inevitably going to restrict the operational area where drones can take-off and land. Hence, there is a need to develop a system to manage landing and take-off areas for drones. Additionally, we proposed this approach due to the lack of justification for the initial location of drones in current research. Therefore, to provide a foundation for future research, we give a justified reason that allows predetermined location of drones with the use of drone ports. Furthermore, we propose an algorithm to optimally place these drone ports to minimize the average distance drones must travel based on a set of potential drone port locations and tasks generated in a given area. Our approach is derived from the Facility Location problem which produces an efficient near optimal solution to place drone ports that reduces the overall drone energy consumption. Secondly, we apply various traveling salesman algorithms to determine the shortest route the drone must travel to visit all the tasks.

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“…This thesis work resulted in three manuscripts, one journal article [7], one conference proceeding [8], and another journal article to be submitted for possible publication.…”

Section: Thesis Contributionmentioning

confidence: 99%

3D deployment of UAVs in wireless networks for traffic offloading and edge computing

Islambouli¹

Self Cite

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Unmanned aerial vehicles (UAVs) have recently emerged as enablers for mul- titude use cases in 5G networks leading to interesting industrial and business applications. 5G networks envision a multi-service network promoting various applications with a distinct set of performance and service demands. In this the- sis, we leverage the high exibility, low-cost, and mobility of UAVs to scale up and improve the e ciency of IoT and mobile networks. We study the utilization of UAVs to increase the capacity and coverage in wireless networks on one side and to extend low computational capabilities and mitigate battery limitations in constrained devices on another side. However, to unlock these promising use cases of UAVs, we address the challenges coupled with UAV utilization mainly 3D deployment and device association. First, we address the problem of deploying multiple UAVs to act as aerial base stations (ABS) in 3D space while autonomously adapting their positions as users move around within the network. We formulate the problem as a mixed integer program and then propose a novel autonomous positioning approach that can e ciently gear the UAV positions in a way to maintain target quality re- quirements. Next, we leverage the mobility and agility of UAVs and use them as mo- bile edge servers or cloudlets to o er computation o oading opportunities to IoT devices. This being said, computation tasks generated by IoT devices can be pro- cessed in less latency and with much lower energy consumption at the devices. To optimally deploy UAVs as mounted cloudlets, we formulate our problem as mixed integer program and then use an e cient meta-heuristic algorithm to generate optimized results for large scale IoT networks. The simulation results presented in this thesis demonstrate the e ectiveness of the proposed solutions and algo- rithms compared to the optimal solutions and related work in the literature for various network scenario

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On-demand deployment of multiple aerial base stations for traffic offloading and network recovery

Cited by 61 publications

References 33 publications

Joint Sub-Carrier and Power Allocation for Efficient Communication of Cellular UAVs

Joint Sub-Carrier and Power Allocation for Efficient Communication of Cellular UAVs

Facility Location Problem Approach for Distributed Drones

3D deployment of UAVs in wireless networks for traffic offloading and edge computing

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