Multiobjective Optimization Approach for Reducing Hovering and Motion Energy Consumptions in UAV-Assisted Collaborative Beamforming

Communications and networks of unmanned aerial vehicles (UAVs) are of paramount importance, owing to their flexible mobility and fast deployment. However, how to enhance the communication efficiency under the restricted on-board energy and transmit power is still one of the most critical problems. I...

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Veröffentlicht in:IEEE internet of things journal Jg. 11; H. 4; S. 7198 - 7213
Hauptverfasser: Liang, Shuang, Yin, Minghao, Sun, Geng, Li, Jiahui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 15.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Antenna arrays
Array signal processing
Autonomous aerial vehicles
Beamforming
Collaboration
Collaborative beamforming (CB)
Communication
Continuity (mathematics)
Energy consumption
energy efficiency
Hovering
Internet of Things
multiobjective optimization problem (MOP)
Multiple objective analysis
Optimization
Three-dimensional displays
unmanned aerial vehicle (UAV) communications
Unmanned aerial vehicles
ISSN:2327-4662, 2327-4662
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Zusammenfassung:Communications and networks of unmanned aerial vehicles (UAVs) are of paramount importance, owing to their flexible mobility and fast deployment. However, how to enhance the communication efficiency under the restricted on-board energy and transmit power is still one of the most critical problems. In this article, we consider a UAV-assisted communication scenario, in which a virtual antenna array (VAA) performed by a swarm of UAVs utilize collaborative beamforming (CB) to communicate with several faraway base stations (BSs). For achieving a superior transmission performance, we formulate a hovering and motion energy consumption multiobjective optimization problem (HMECMOP) of UAV-assisted CB to simultaneously minimize the total hovering and motion energy consumptions of UAVs by jointly optimizing the positions, excitation current weights of UAVs, and the order of communicating with different BSs. Moreover, the formulated HMECMOP is analyzed and proven as an NP-hard and classical hybrid multiobjective optimization problem (MOP) with a complex solution vector that contains continuous and discrete variables. Thus, we propose an improved multiobjective multiverse optimizer (IMOMVO), which uses the vertical and horizontal renewal strategy and nearest neighbor procedure (NNP) to solve the complex HMECMOP. Extensive simulations are carried out to demonstrate that the proposed algorithm can effectively reduce the energy consumption of UAVs communicating with multiple remote BSs so that improving the communication performance.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3315708