Aerial Energy Orchestration for Heterogeneous UAV-Assisted Wireless Communications
- Authors
- Lakew, Demeke Shumeye; Na, Woongsoo; Dao, Nhu-Ngoc; Cho, Sungrae
- Issue Date
- Jun-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Aerial energy sharing; Batteries; Internet of Things; MIMO communication; Solar energy; Trajectory; unmanned aerial vehicle (UAV)-assisted communications; Unmanned aerial vehicles; wireless charging; Wireless networks
- Citation
- IEEE Systems Journal, v.16, no.2, pp 2383 - 2384
- Pages
- 2
- Journal Title
- IEEE Systems Journal
- Volume
- 16
- Number
- 2
- Start Page
- 2383
- End Page
- 2384
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47519
- DOI
- 10.1109/JSYST.2021.3075316
- ISSN
- 1932-8184
1937-9234
- Abstract
- The integration of unmanned aerial vehicles (UAVs) into future-generation wireless networks is a promising strategy for complementing terrestrial communication infrastructures that will lead to capacity and coverage improvements. Unfortunately, the energy limitations of the existing off-the-shelf small-size UAVs are significant challenges, especially during long missions. In this article, first, we investigate the communication model of a heterogeneous UAV-assisted wireless network that consists of multiple energy-constrained battery-powered small-size UAVs (a.k.a. follower UAVs) and a large-size UAV (a.k.a. dispatcher UAV) with solar energy harvesting capability. Subsequently, we propose an aerial energy sharing algorithm to improve the lifetime of the system by orchestrating energy balancing among follower UAVs. In particular, the dispatcher UAV having large energy capacity owing to its battery size and solar energy harvesting capability shares its energy with follower UAVs using radio-frequency-based power transfer through beamforming. The proposed energy sharing algorithm considers the placement, available battery capacity, and mission execution time of the follower UAVs to decide the energy sharing points with optimum efficiency. Extensive simulations show that our proposed algorithm outperforms existing algorithms in terms of charging efficiency and energy balancing among UAVs. IEEE
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