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

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-14 DOI:10.1109/TNSE.2025.3529200

Xiangbin Yu;Xinyi Zhang;Yun Rui;Xiaoyu Dang;Guoqing Jia;Mohsen Guizani

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引用次数: 0

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 groundusers. 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.

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基于NOMA的无人机辅助MEC网络联合资源分配与三维位置优化

本文研究了在不完全连续干扰消除情况下，无人机（UAV）辅助移动边缘计算（MEC）网络非正交多址（NOMA）计算效率优化问题。具体而言，制定了资源分配（RA）和三维（3D）位置的联合设计方案，以提高行政效益，同时确保地面使用者的公平性。特别地，我们采用最大最小公平性准则，通过两步优化方法对波束形成（BF）、功率分配（PA）、本地CPU频率和无人机位置进行联合优化。即，我们首先使用基于交替优化和凹凸过程方法的高效迭代算法来优化三维位置。然后，采用基于分块坐标下降法、次梯度法和凸优化工具的高效迭代算法求解BF、PA和CPU频率的联合设计；在此基础上，提出了一种复杂度较低的次优PA方案，该方案每次迭代都具有封闭表达式。仿真结果表明，提出的两种关节RA和位置设计方案是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.