Joint Power and 3D Trajectory Optimization for UAV-enabled Wireless Powered Communication Networks with Obstacles

Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCNs) are promising technologies in 5G/6G wireless communications, while there are several challenges about UAV power allocation and scheduling to enhance the energy utilization efficiency, considering the existence of o...

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Vydáno v:IEEE transactions on communications Ročník 71; číslo 4; s. 1
Hlavní autoři: Pan, Hongyang, Liu, Yanheng, Sun, Geng, Fan, Junsong, Liang, Shuang, Yuen, Chau
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Autonomous aerial vehicles
Barriers
Communication networks
Communications networks
Efficiency
energy consumption
Energy utilization
Genetic algorithms
non-dominated sorting genetic algorithm-II
Normal distribution
Operators (mathematics)
Optimization
Particle swarm optimization
Power management
Renewable energy sources
Resource management
Sorting algorithms
Three-dimensional displays
Trajectory optimization
unmanned aerial vehicle
Unmanned aerial vehicles
Uplink
Wireless communications
Wireless networks
Wireless powered communication networks
ISSN:0090-6778, 1558-0857
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Shrnutí:Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCNs) are promising technologies in 5G/6G wireless communications, while there are several challenges about UAV power allocation and scheduling to enhance the energy utilization efficiency, considering the existence of obstacles. In this work, we consider a UAV-enabled WPCN scenario that a UAV needs to cover the ground wireless devices (WDs). During the coverage process, the UAV needs to collect data from the WDs and charge them simultaneously. To this end, we formulate a joint-UAV power and three-dimensional (3D) trajectory optimization problem (JUPTTOP) to simultaneously increase the total number of the covered WDs, increase the time efficiency, and reduce the total flying distance of UAV so as to improve the energy utilization efficiency in the network. Due to the difficulties and complexities, we decompose it into two sub optimization problems, which are the UAV power allocation optimization problem (UPAOP) and UAV 3D trajectory optimization problem (UTTOP), respectively. Then, we propose an improved non-dominated sorting genetic algorithm-II with K -means initialization operator and Variable dimension mechanism (NSGA-II-KV) for solving the UPAOP. For UTTOP, we first introduce a pretreatment method, and then use an improved particle swarm optimization with Normal distribution initialization, Genetic mechanism, Differential mechanism and Pursuit operator (PSO-NGDP) to deal with this sub optimization problem. Simulation results verify the effectiveness of the proposed strategies under different scales and settings of the networks.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2023.3240697