Coverage Optimization with a Dynamic Network of Drone Relays

Arribas, Edgar; Mancuso, Vincenzo; Cholvi, Vicent

doi:10.1109/tmc.2019.2927335

Cited by 48 publications

(26 citation statements)

References 148 publications

(269 reference statements)

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“…The authors of [230] proposed an optimization framework of 3D hovering placement of relay UAVs assisting a ground cellular network. This framework considers both realistic UAV connectivity and inter-UAV interference constraints.…”

Section: E Sdn-based Cellular Communicationsmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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Section: E Sdn-based Cellular Communicationsmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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“…The studies in [ 19 , 20 , 21 ] considered the cellular networking scenario where UAVs were deployed to support the connectivity of existing terrestrial wireless networks. The study in [ 19 ] proposed an approach to deploy multi-UAVs coexisting with a ground base station to provide wireless coverage for users in a crowded region.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, the authors developed a cooperative UAV clustering approach to offload ground mobile terminals from ground cellular base stations to cooperative UAV clusters. Meanwhile, the work in [ 21 ] addressed the issue in the backhaul links between drones and ground base stations. More specifically, the authors in [ 21 ] proposed a 3D UAV placement and trajectory model.…”

Section: Related Workmentioning

confidence: 99%

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Power-Efficient Wireless Coverage Using Minimum Number of UAVs

Sawalmeh

Othman

Liu

et al. 2021

Sensors

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Unmanned aerial vehicles (UAVs) can be deployed as backup aerial base stations due to cellular outage either during or post natural disaster. In this paper, an approach involving multi-UAV three-dimensional (3D) deployment with power-efficient planning was proposed with the objective of minimizing the number of UAVs used to provide wireless coverage to all outdoor and indoor users that minimizes the required UAV transmit power and satisfies users’ required data rate. More specifically, the proposed algorithm iteratively invoked a clustering algorithm and an efficient UAV 3D placement algorithm, which aimed for maximum wireless coverage using the minimum number of UAVs while minimizing the required UAV transmit power. Two scenarios where users are uniformly and non-uniformly distributed were considered. The proposed algorithm that employed a Particle Swarm Optimization (PSO)-based clustering algorithm resulted in a lower number of UAVs needed to serve all users compared with that when a K-means clustering algorithm was employed. Furthermore, the proposed algorithm that iteratively invoked a PSO-based clustering algorithm and PSO-based efficient UAV 3D placement algorithms reduced the execution time by a factor of ≈1/17 and ≈1/79, respectively, compared to that when the Genetic Algorithm (GA)-based and Artificial Bees Colony (ABC)-based efficient UAV 3D placement algorithms were employed. For the uniform distribution scenario, it was observed that the proposed algorithm required six UAVs to ensure 100% user coverage, whilst the benchmarker algorithm that utilized Circle Packing Theory (CPT) required five UAVs but at the expense of 67% of coverage density.

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“…In this article, we assume that two types of UAVs compose the flying network: Flying Access Points (FAPs) and a single Flying Communications Relay (FCR). In the literature, several solutions have been proposed for the positioning of FAPs, aiming at enhancing the wireless coverage, the number of ground users served [5][6][7][8], or the QoS offered [3,[9][10][11][12][13]. However, the positioning of the FCR UAV has not received equal attention.…”

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confidence: 99%

Joint Energy and Performance Aware Relay Positioning in Flying Networks

Rodrigues¹,

Coelho²,

Ricardo³

et al. 2022

Preprint

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<p>Unmanned Aerial Vehicles (UAVs) have emerged as suitable platforms for transporting and positioning communications nodes on demand, including Wi-Fi Access Points and cellular Base Stations. This paved the way for the deployment of flying networks capable of temporarily providing wireless connectivity and reinforcing the coverage and capacity of existing networks anywhere, anytime. Several solutions have been proposed in the literature for the positioning of UAVs that act as Flying Access Points (FAPs). Yet, the positioning of Flying Communications Relays (FCRs) in charge of forwarding the traffic to/from the Internet has not received equal attention. A major challenge in flying networks is the UAVs endurance. Since the UAVs are typically powered by on-board batteries with limited capacity, whose energy is used for communications and propulsion, the UAVs need to land frequently for recharging or replacing their batteries, limiting the flying network availability. State of the art works are focused on optimizing both the flying network performance and the energy-efficiency from the communications point of view, but do not consider the energy spent for the UAV propulsion. Yet, the energy spent for communications is typically negligible when compared with the energy spent for the UAV propulsion.</p><p>In order to address the FCR UAV positioning and energy-efficiency challenges, we have proposed the Energy-aware RElay Positioning (EREP) algorithm. EREP defines the trajectory and speed of the FCR UAV that minimize the energy spent for the UAV propulsion. However, since EREP considers a theoretical radio propagation model for computing the minimum Signal-to-Noise Radio (SNR) values that allow to meet the FAPs traffic demand, this may lead to network performance degradation in real-world networking scenarios, especially due to the FCR UAV movement. In this article, we propose the Energy and Performance Aware relay Positioning (EPAP) algorithm. Built upon the EREP algorithm, EPAP defines target performance-aware SNR values for the wireless links established between the FCR UAV and the FAPs and, based on that, computes the trajectory to be completed by the FCR UAV, so that the energy spent for the UAV propulsion is minimized. EPAP was evaluated in terms of both the flying network performance and the FCR UAV endurance, considering multiple networking scenarios. Simulation results show gains up to 25% in the FCR UAV endurance, while not compromising the Quality of Service offered by the flying network.</p>

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Coverage Optimization with a Dynamic Network of Drone Relays

Abstract: Optimization with a Dynamic Network of Drone Relays". Under revision in Transactions on Networking.

Cited by 48 publications

References 148 publications

Softwarization of UAV Networks: A Survey of Applications and Future Trends

Softwarization of UAV Networks: A Survey of Applications and Future Trends

Power-Efficient Wireless Coverage Using Minimum Number of UAVs

Joint Energy and Performance Aware Relay Positioning in Flying Networks

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