Two-Stage Deep Energy Optimization in IRS-Assisted UAV-Based Edge Computing Systems

doi:10.1109/TMC.2024.3461719

科研成果详情

题名	Two-Stage Deep Energy Optimization in IRS-Assisted UAV-Based Edge Computing Systems
作者	Wu，Jianqiu 1; Yu，Zhongyi 1; Guo，Jianxiong2,3 ; Tang，Zhiqing 2; Wang，Tian2 ; Jia，Weijia 2,3
发表日期	2025
发表期刊	IEEE Transactions on Mobile Computing
ISSN/eISSN	1536-1233
卷号	24 期号:1 页码:449-465
摘要	Integrating wireless-powered Mobile Edge Computing (MEC) with Unmanned Aerial Vehicles (UAVs) leverages computation offloading services for mobile devices, significantly enhancing the mobility and control of MEC networks. However, current research has not focused on customizing system designs for Terahertz (THz) communication networks. When dealing with THz communication, one must account for blockage vulnerability due to severe THz wave propagation attenuation and insufficient diffraction. The Intelligent Reflecting Surface (IRS) can effectively address these limitations in the model, enhancing spectrum efficiency and coverage capabilities while reducing blockage vulnerability in THz networks. In this paper, we introduce an upgraded MEC system that integrates IRS and UAVs into THz communication networks, focusing on a binary offloading policy for studying the computation offloading problem. Our primary objective is to optimize the energy consumption of both UAVs and User Electronic Devices, alongside refining the phase shift of the IRS reflector. The problem is a Mixed Integer Non-Linear Programming problem known as NP-hard. To tackle this challenge, we propose a two-stage deep learning-based optimization framework named Iterative Order-Preserving Policy Optimization (IOPO). Unlike exhaustive search methods, IOPO continually updates offloading decisions through an order-preserving quantization method, thereby accelerating convergence and reducing computational complexity, especially when handling complex problems with extensive solution spaces. The numerical results demonstrate that the proposed algorithm significantly improves energy efficiency and achieves near-optimal performance compared to benchmark methods.
关键词	deep learning intelligent reflective surface Mobile edge computing terahertz communications unmanned aerial vehicles
DOI	10.1109/TMC.2024.3461719
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-86000373453
引用统计
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/13744
专题	北师香港浸会大学
通讯作者	Guo，Jianxiong
作者单位	1.BNU-HKBU United International College,Guangdong Key Lab of AI and Multi-Modal Data Processing,Department of Computer Science,Zhuhai,519087,China 2.Beijing Normal University,Advanced Institute of Natural Sciences,Zhuhai,519087,China 3.BNU-HKBU United International College,Guangdong Key Lab of AI and Multi-Modal Data Processing,Zhuhai,519087,China
第一作者单位	北师香港浸会大学
通讯作者单位	北师香港浸会大学
推荐引用方式 GB/T 7714	Wu，Jianqiu,Yu，Zhongyi,Guo，Jianxionget al. Two-Stage Deep Energy Optimization in IRS-Assisted UAV-Based Edge Computing Systems[J]. IEEE Transactions on Mobile Computing, 2025, 24(1): 449-465.
APA	Wu，Jianqiu, Yu，Zhongyi, Guo，Jianxiong, Tang，Zhiqing, Wang，Tian, & Jia，Weijia. (2025). Two-Stage Deep Energy Optimization in IRS-Assisted UAV-Based Edge Computing Systems. IEEE Transactions on Mobile Computing, 24(1), 449-465.
MLA	Wu，Jianqiu,et al."Two-Stage Deep Energy Optimization in IRS-Assisted UAV-Based Edge Computing Systems". IEEE Transactions on Mobile Computing 24.1(2025): 449-465.