文献类型：期刊文献

英文题名：Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

作者：Zhang, Hongxia[1];Xi, Shiyu[1];Jiang, Hongzhao[2];Shen, Qi[3,4];Shang, Bodong[5];Wang, Jian[6]

第一作者：Zhang, Hongxia

通讯作者：Shen, Q[1];Shen, Q[2]

机构：[1]China Univ Petr East China, Qingdao Inst Software, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China;[2]Sixth Res Inst China Elect Corp, Beijing 100083, Peoples R China;[3]Beijing Union Univ, Teachers Coll, Beijing 100023, Peoples R China;[4]Beijing Union Univ, Institue Sci & Technol Educ, Beijing 100023, Peoples R China;[5]Eastern Inst Adv Study, Coll Informat Sci & Technol, Ningbo 315200, Peoples R China;[6]China Univ Petr East China, Coll Sci, Qingdao 266580, Peoples R China

第一机构：China Univ Petr East China, Qingdao Inst Software, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Teachers Coll, Beijing 100023, Peoples R China;[2]corresponding author), Beijing Union Univ, Institue Sci & Technol Educ, Beijing 100023, Peoples R China.|[11417]北京联合大学;[1141711]北京联合大学师范学院;

年份：2023

卷号：7

期号：6

外文期刊名：DRONES

收录：;EI(收录号：20243416893321);Scopus(收录号：2-s2.0-85163834502);WOS:【SCI-EXPANDED(收录号:WOS:001014165500001)】；

基金：This work is partially supported by the Natural Science Foundation of Shandong Province under Grant ZR2020MF006 and ZR2022LZH015, R & D Program of Beijing Municipal Education Commission under Grant KM202211417014, Academic Research Projects of Beijing Union University under Grant ZK20202215 and Open Foundation of State Key Laboratory of Integrated Services Networks (Xidian University) under Grant ISN23-09.

语种：英文

外文关键词：deep reinforcement learning; edge computing; task offloading

摘要：In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations.