Research on multi-UAV energy consumption optimization algorithm for cellular-connected network

被引：0
作者：
Xia J. [1 ,2 ]
Liu Y. [3 ]
Tan L. [4 ]
机构：
[1] School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing
[2] Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing
[3] School of Software, Nanjing University of Information Science and Technology, Nanjing
[4] School of Computer Science, Nanjing University of Information Science and Technology, Nanjing
来源：
Tongxin Xuebao/Journal on Communications | 2023年 / 44卷 / 02期
基金：
国家重点研发计划;
关键词：
DDQN; digital twins; SCA; task unloading; UAV;
D O I：
10.11959/j.issn.1000-436x.2023025
中图分类号：
学科分类号：
摘要：
In complex time-varying environment, the ground base station (GBS) may not assist the UAV. Therefore, a mobile edge computing (MEC) cellular-connected network based on digital twin (DT) technology was studied. Given the efficiency of multi-UAV, multiple high-altitude balloon (HAB) equipped with MEC servers were introduced. On this basis, an energy minimization problem for all UAV was proposed, and a multi-UAV trajectory optimization and resource allocation scheme was presented to solve it. The double deep Q-network (DDQN) was applied to handle the association between multi-UAV and multi-HAB, and the multi-UAV trajectory and computing resource allocation were jointly optimized by the successive convex approximation (SCA) and the block coordinate descent (BCD). Simulation experiments verify the feasibility and effectiveness of the proposed algorithm. The system energy consumption is reduced by 30%, better than the comparison algorithms. © 2023 Editorial Board of Journal on Communications. All rights reserved.
引用
收藏
页码：187 / 197
页数：10
相关论文
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