Resource Allocation for Solar Powered UAV Communication Systems

Sun, Yan; Ng, Derrick Wing Kwan; Xu, Dongfang; Dai, Linglong; Schober, Robert

doi:10.1109/spawc.2018.8445944

Cited by 31 publications

(48 citation statements)

References 24 publications

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“…UAV-enabled information dissemination or multicasting is one of the most important applications. To provide communication services to multiple downlink users, [6] studied a multiple access scheme with a solar-powered UAV. Yet, [6] only considered the system throughput maximization without taking into account the quality of services (QoS) requirement in communications.…”

Section: Introductionmentioning

confidence: 99%

“…To provide communication services to multiple downlink users, [6] studied a multiple access scheme with a solar-powered UAV. Yet, [6] only considered the system throughput maximization without taking into account the quality of services (QoS) requirement in communications. [7] studied the mission completion time minimization of a cellular-enabled UAV communication system which guarantees the QoS of connectivity between the UAV and ground base stations.…”

Section: Introductionmentioning

confidence: 99%

“…[7] studied the mission completion time minimization of a cellular-enabled UAV communication system which guarantees the QoS of connectivity between the UAV and ground base stations. However, utilizing UAV to provide wireless communications to multiple users while ensuring the minimum data rate required for each user is not considered in [6], and [7].…”

Section: Introductionmentioning

confidence: 99%

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Joint Trajectory and Resource Allocation Design for UAV Communication Systems

Wei

Yang

et al. 2018

2018 IEEE Globecom Workshops (GC Wkshps)

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In this paper, we investigate resource allocation design for unmanned aerial vehicle (UAV)-enabled communication systems, where a UAV is dispatched to provide communications to multiple user nodes. Our objective is to maximize the communication system throughput by jointly optimizing the subcarrier allocation policy and the trajectory of the UAV, while taking into account the minimum required data rate for each user node, no-fly zones (NFZs), the maximum UAV cruising speed, and initial/final UAV locations. The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationallyefficient iterative algorithm is proposed to obtain a locally optimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm approaches closely to that of the system without NFZ. In addition, the proposed algorithm can achieve a significant throughput gain compared to various benchmark schemes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Trajectory and Resource Allocation Design for UAV Communication Systems

Wei

Yang

et al. 2018

2018 IEEE Globecom Workshops (GC Wkshps)

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“…This tradeoff does not exist in conventional UAV communication systems and the results derived in [5]- [9] are only applicable to non-energy harvesting UAV communication systems. In our previous work [1], we studied the resource allocation for multicarrier (MC) solar-powered UAV communication systems.…”

mentioning

confidence: 99%

Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems

Sun

et al. 2019

IEEE Trans. Commun.

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382

214

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In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the three-dimensional (3D) aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an offline resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixed-integer non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service (QoS) requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on online algorithm design which only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the offline scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on successive convex approximation. Our simulation results reveal that both proposed online schemes closely approach the performance of the benchmark offline scheme and substantially outperform two baseline schemes. Furthermore, our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descents again to a lower altitude to reduce the path loss of the communication links to the users it serves.

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“…20: Compute the optimal p * k,m using (10), where q * k is given by (12). 21 . Finally, computing Ξ ( v) for a fixed v = v would involve the travelling salesman problem, which requires a complexity of O (M − 1) 2 · 2 M−1 in the worst case [46].…”

mentioning

confidence: 99%

Backscatter Data Collection With Unmanned Ground Vehicle: Mobility Management and Power Allocation

Wang

Xia

2019

IEEE Trans. Wireless Commun.

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Collecting data from massive Internet of Things (IoT) devices is a challenging task, since communication circuits are power-demanding while energy supply at IoT devices is limited. To overcome this challenge, backscatter communication emerges as a promising solution as it eliminates radio frequency components in IoT devices. Unfortunately, the transmission range of backscatter communication is short. To facilitate backscatter communication, this work proposes to integrate unmanned ground vehicle (UGV) with backscatter data collection. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper studies energy minimization under a joint graph mobility and backscatter communication model. With the joint model, the mobility management and power allocation problem unfortunately involves nonlinear coupling between discrete variables brought by mobility and continuous variables brought by communication. Despite the optimization challenges, an algorithm that theoretically achieves the minimum energy consumption is derived, and it leads to automatic trade-off between spending energy on moving versus on communication in the UGV backscatter system. Simulation results show that if the noise power is small (e.g., ≤ −100 dBm), the UGV should collect the data with small movements. However, if the noise power is increased to a larger value (e.g., −60 dBm), the UGV should spend more motion energy to get closer to IoT users.Index Terms-Backscatter communication, Internet of Things (IoT), mixed integer optimization, quality-of-service (QoS), unmanned ground vehicle (UGV).

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Resource Allocation for Solar Powered UAV Communication Systems

Cited by 31 publications

References 24 publications

Joint Trajectory and Resource Allocation Design for UAV Communication Systems

Joint Trajectory and Resource Allocation Design for UAV Communication Systems

Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems

Backscatter Data Collection With Unmanned Ground Vehicle: Mobility Management and Power Allocation

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