Control of Communication Networks for Teams of UAVs

Kopeikin, Andrew; Ponda, Sameera S.; Inalhan, Gökhan

doi:10.1007/978-90-481-9707-1_19

Cited by 7 publications

(15 citation statements)

References 39 publications

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“…where L i is the average path loss between the relay UAV and u i , f c is the carrier frequency, κ is the path loss exponent varies 2-6 [8], η 1 and η 2 (η 2 > η 1 ) are the excessive path loss coefficients in LoS and NLoS cases, c is the speed of light, and d i is the distance between u i and the UAV. Typically, given only the locations of the UAVs and devices, it is not possible to exactly determine which path loss type (LoS/NLoS) is experienced by the device-UAV link.…”

Section: Channel Approximationmentioning

confidence: 99%

“…The qualities of wireless communication channels, such as capacity, delay, bit-error-rate, etc. are closely related to RSS [8], they are used as the channel performance metric in this work and could be given as follows:…”

Section: Channel Approximationmentioning

confidence: 99%

“…However, increasing distance and obstructing due to terrain or surroundings (such as trees, building, etc.) negatively affects the quality of wireless communication severely because signal strength decreases exponentially [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Mobility Control of Unmanned Aerial Vehicle as Communication Relay to Optimize Ground-to-Air Uplinks

Gao

Wan

2020

Sensors

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In recent years, unmanned aerial vehicles (UAVs) have been considered an ideal relay platform for enhancing the communication between ground agents, because they fly at high altitudes and are easy to deploy with strong adaptabilities. Their maneuvering allows them to adjust their location to optimize the performance of links, which brings out the relay UAV autonomous mobility control problem. This work addressed the problem in a novel scene with mobile agents and completely unknown wireless channel properties, using only online measured information of received signal strength (RSS) and agent positions. The problem is challenging because of the unknown and dynamic radio frequency (RF) environment cause by agents and UAV maneuvering. We present a framework for both end-to-end communication and multi-agent-inter communication applications, and focus on proposing: (1) least square estimation-based channel approximation with consideration of environment effects and, (2) gradient-based optimal relay position seeking. Simulation results show that considering the environmental effects on channel parameters is meaningful and beneficial in using UAV as relays for the communication of multiple ground agents, and validate that the proposed algorithms optimizes the network performance by controlling the heading of the UAV.

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Section: Channel Approximationmentioning

confidence: 99%

Section: Channel Approximationmentioning

confidence: 99%

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Mobility Control of Unmanned Aerial Vehicle as Communication Relay to Optimize Ground-to-Air Uplinks

Gao

Wan

2020

Sensors

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“…It will be the main communication mode in the future or the next generation of communication datalinks. At present, three kinds of communication ad hoc networks (mobile ad hoc networks, wireless sensor networks, and wireless mesh networks) can be utilized in a UAV swarm owing to their mobility and network topology dynamics, multiple-hop transmission, and self-organization [ 37 , 38 , 39 ]. Moreover, these networks are rather robust, so that a single-node failure in the network has no effect on the performance of the entire network.…”

Section: Uav Swarming Systemmentioning

confidence: 99%

A Complex Network Theory-Based Modeling Framework for Unmanned Aerial Vehicle Swarms

Wang

Zhang

et al. 2018

Sensors

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Unmanned aerial vehicle (UAV) swarms is an emerging technology that will significantly expand the application areas and open up new possibilities for UAVs, while also presenting new requirements for the robustness and reliability of the UAV swarming system. However, its complex and dynamic characteristics make it extremely challenging and uncertain to model such a system. In this study, to reach a full understanding of the swarming system, a modeling framework based on complex network theory is presented. First, the scope of work is identified from the point of view of control algorithms considering the dynamics and research novelty of the development of UAV swarming control strategy and three control structures consisting of three interdependent network layers are proposed. Second, three algorithms that systematically build the modeling framework considering all characteristics of the system are also developed. Finally, some network measurements are introduced by adjusting the fundamental ones into the UAV swarming system. The proposed framework is applied to a case study to illustrate the visualization models and estimate the statistical characteristics of the proposed networks with static and dynamic topology analysis. Furthermore, a simple demonstration of the robustness evaluation of the network is also presented. The networks obtained from this framework can be used to further analyze the robustness or reliability of a UAV swarming system in a high-confrontation battlefield environment the effect of cascading failure in ad-hoc network on system.

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“…One promising solution for such scenarios is to deploy unmanned aerial vehicles (UAVs) for providing backhaul connectivity to the SBSs [5] and [6]. Due to their rapid and flexible deployment capabilities, mobility, ability to fly above obstacles, and relatively low cost, UAVs have received considerable interest for different applications in wireless communications, and in particular, as communication relays [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Network Formation in the Sky: Unmanned Aerial Vehicles for Multi-Hop Wireless Backhauling

Challita

Saad

2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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To reap the benefits of dense small base station (SBS) deployment, innovative backhaul solutions are needed in order to manage scenarios in which high-speed ground backhaul links are either unavailable or limited in capacity. In this paper, a novel backhaul scheme that utilizes unmanned aerial vehicles (UAVs) as an on-demand flying network linking ground SBSs and the core network is proposed. The design of the aerial backhaul scheme is formulated as a network formation game among UAVs that seek to form a multi-hop backhaul network in the air. To solve this game, a myopic network formation algorithm which reaches a pairwise stable network upon convergence, is introduced. The proposed network formation algorithm enables the UAVs to form the necessary multi-hop backhaul network in a decentralized manner thus adapting the backhaul architecture to the dynamics of the network. Simulation results show that the proposed network formation algorithm achieves substantial performance gains in terms of both rate and delay reaching, respectively, up to 40% and 41% compared to the formation of direct communication links with the gateway node (for a network with 15 UAVs).

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Control of Communication Networks for Teams of UAVs

Cited by 7 publications

References 39 publications

Mobility Control of Unmanned Aerial Vehicle as Communication Relay to Optimize Ground-to-Air Uplinks

Mobility Control of Unmanned Aerial Vehicle as Communication Relay to Optimize Ground-to-Air Uplinks

A Complex Network Theory-Based Modeling Framework for Unmanned Aerial Vehicle Swarms

Network Formation in the Sky: Unmanned Aerial Vehicles for Multi-Hop Wireless Backhauling

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