Item

Joint Resource Allocation and 3D-Position Optimization for UAV-Assisted MEC Network With NOMA

Yu, Xiangbin
Zhang, Xinyi
Rui, Yun
Dang, Xiaoyu
Jia, Guoqing
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
In this article, the computation efficiency (CE) optimization of unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) is addressed in the presence of imperfect successive interference cancelation. Specifically, joint design schemes of resource allocation (RA) and three-dimensional (3D) position are developed to improve the CE while ensuring the fairness of ground users. In particular, we apply the max-min fairness criterion and optimize the beamforming (BF), power allocation (PA), local CPU frequency and UAV position jointly via two-step optimization method. Namely, we first optimize the 3D position by using an efficient iteration algorithm based on the alternating optimization and concave-convex procedure methods. Then, the joint design of BF, PA and CPU frequency is solved by an efficient iteration algorithm based on the block coordinate descent, sub- gradient methods and convex optimization tool. Additionally, a lower-complexity suboptimal PA scheme with closed-form expression for each iteration is developed. Simulation results indicate that the proposed two design schemes of joint RA and position are effective.
Citation
X. Yu, X. Zhang, Y. Rui, X. Dang, G. Jia and M. Guizani, "Joint Resource Allocation and 3D-Position Optimization for UAV-Assisted MEC Network With NOMA," in IEEE Transactions on Network Science and Engineering, doi: 10.1109/TNSE.2025.3529200.
Source
IEEE Transactions on Network Science and Engineering
Conference
Keywords
Autonomous aerial vehicles, Optimization, NOMA, Three-dimensional displays, Resource management, Energy consumption, Trajectory, Symbols, Interference, Delays
Subjects
Source
Publisher
IEEE
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