Energy-efficient maneuvering and communication of a single UAV-based relay

Choi, Dae Hyung; Kim, Seong Hwan; Sung, Dan Keun

doi:10.1109/taes.2013.130074

Cited by 120 publications

(56 citation statements)

References 14 publications

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“…The communications provider must assign locations to drones so that the desired mobile connectivity is achieved at minimum cost or with the minimum number of drones . Because data transmission and setup operations to establish links consume the energy of drones, a common objective function is to maximize energy efficiency or minimize energy to achieve the desired communication quality . Alternatively, the number of established communication links and the quality of communication , such as packet transmission delay or the coverage probability , should be optimized given limited resources, such as the limited energy of the drone.…”

Section: Planning Drone Operationsmentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

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Section: Planning Drone Operationsmentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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“…In the m-th iteration, let U m−1 and L m−1 respectively denote the upper bound and lower bound of Q l . For Q m = U m−1 + L m−1 2, with given c l−1 a,t , v l−1 a,t April 5, 2019 DRAFT and P l a,t obtained by solving the problem in (42), the convex problem can be formulated as find c m a,t , v m a,t , a m a,t (44) subject to (10), (11), (12), (23), (24), (31), (37), (38), (39) where P a,t , c a,t , v a,t , a a,t , Q are replaced with P l a,t , c m a,t , v m a,t , a m a,t , Q m , respectively. Besides, v r a,t and c r a,t are replaced with v l−1 a,t and c l−1 a,t , respectively.…”

Section: Initializationmentioning

confidence: 99%

Maritime Coverage Enhancement Using UAVs Coordinated With Hybrid Satellite-Terrestrial Networks

Feng

Chen

et al. 2020

IEEE Trans. Commun.

137

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Due to its agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for ondemand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Under the typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice, and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework. Index TermsHybrid satellite-terrestrial network, maritime communications, power allocation, trajectory, unmanned aerial vehicle (UAV). X. Li, W. Feng (corresponding author), and N. Ge are with the Beijing the UAV's maximum velocity and/or maximum acceleration, the trajectory of UAV was optimized for maximum throughput or minimum UAV periodic flight duration, or optimal energy efficiency [18]-[23]. These works [11]-[23] mainly considered static users. For mobile users, the ergodic achievable rate was maximized by dynamically adjusting the UAV heading [24]-[26]. Intuitively in the maritime scenario, the UAV's trajectory should adaptively cater to the mobility of ships, providing an accompanying broadband coverage, which however remains elusive. 2) Coexistence of UAVs and TBSs: In addition to UAV-only models, the coexistence of UAVs and TBSs was investigated in [27]-[31]. For rotary-wing UAVs, the TBS can be used as a hub to connect UAVs to the network [27]. In this case, the access link and backhaul link should be jointly optimized to maximize the sum rate. In [28], the UAV-based multi-hop backhaul network was formulated to adapt to the dynamics of the network. Outage probability is also an important issue for the coexistence of UAVs and TBSs [29]-[31]. In [30], the outage probability was minimized. In [31], the throughput was maximized subject to the maximum outage probability constraint. For the maritime scenario, the TBS is the primary choice for UAV backhaul, due to their high-speed transmission rate. 3) Coexistence of UAVs and Satellites: More recently, the integration of UAVs and satellites has been investigated in [32]-[37]. Particularly, the aut...

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“…UAV trajectory planning is studied in [17,18,19,20], where the UAV acts as a communication relay for connecting the sensor nodes. Network throughput and communication delay can be improved by the motion control of the UAV, e.g., heading, velocity, or radius of the flight trajectory.…”

Section: Uav Trajectory Planningmentioning

confidence: 99%

On-Board Deep Q-Network for UAV-Assisted Online Power Transfer and Data Collection

Li¹,

Tovar³

et al. 2019

IEEE Trans. Veh. Technol.

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Unmanned Aerial Vehicles (UAVs) with Microwave Power Transfer (MPT) capability provide a practical means to deploy a large number of wireless powered sensing devices into areas with no access to persistent power supplies. The UAV can charge the sensing devices remotely and harvest their data. A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the sensing devices, while up-to-date knowledge on battery level and data queue of the devices is not available at the UAV. In this paper, an on-board deep Q-network is developed to minimize the overall data packet loss of the sensing devices, by optimally deciding the device to be charged and interrogated for data collection, and the instantaneous patrolling velocity of the UAV. Specifically, we formulate a Markov Decision Process (MDP) with the states of battery level and data queue length of sensing devices, channel conditions, and waypoints given the trajectory of the UAV; and solve it optimally with Q-learning. Furthermore, we propose the on-board deep Q-network that can enlarge the state space of the MDP, and a deep reinforcement learning based scheduling algorithm that asymptotically derives the optimal solution online, even when the UAV has only outdated knowledge on the MDP states. Numerical results demonstrate that the proposed deep reinforcement learning algorithm reduces the packet loss by at least 69.2%, as compared to existing non-learning greedy algorithms.

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Energy-efficient maneuvering and communication of a single UAV-based relay

Cited by 120 publications

References 14 publications

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Maritime Coverage Enhancement Using UAVs Coordinated With Hybrid Satellite-Terrestrial Networks

On-Board Deep Q-Network for UAV-Assisted Online Power Transfer and Data Collection

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