Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；College of Science and Engineering（科学与工程学院）；Division of Information and Computing Technology（信息与计算技术系）；College of Engineering, Qatar University（卡塔尔大学工程学院）
Muhammet Hevesli, Abegaz Mohammed Seid, Aiman Erbad, Mohamed Abdallah
Mobile edge computing (MEC)-enabled air-ground networks are a key component of 6G, employing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS) to provide dynamic services to ground IoT devices (IoTDs). These IoTDs support real-time applications (e.g., multimedia and Metaverse services) that demand high computational resources and strict quality of service (QoS) guarantees in terms of latency and task queue management. Given their limited energy and processing capabilities, IoTDs rely on UAVs and HAPS to offload tasks for distributed processing, forming a multi-tier MEC system. This paper tackles the overall energy minimization problem in MEC-enabled air-ground integrated networks (MAGIN) by jointly optimizing UAV trajectories, computing resource allocation, and queue-aware task offloading decisions. The optimization is challenging due to the nonconvex, nonlinear nature of this hierarchical system, which renders traditional methods ineffective. We reformulate the problem as a multi-agent Markov decision process (MDP) with continuous action spaces and heterogeneous agents, and propose a novel variant of multi-agent proximal policy optimization with a Beta distribution (MAPPO-BD) to solve it. Extensive simulations show that MAPPO-BD outperforms baseline schemes, achieving superior energy savings and efficient resource management in MAGIN while meeting queue delay and edge computing constraints.
Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond
University of Electronic Science and Technology of China（电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula大都会数字工程中心）
Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Machano-Electronic Engineering, Xidian University（西安电子科技大学机电工程学院）；School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
Hunan Normal University（湖南师范大学）；Northumbria University（诺森比亚大学）；Hunan University of Technology and Business（湖南工商大学）；Queen Mary University of London（伦敦玛丽女王大学）；Changchun Institute of Technology（长春工程学院）；University of Essex（埃塞克斯大学）
Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning
Faculty of Information and Communication Technology National Academy of Education Management（信息与通信技术学院国家教育管理 academy）；School of Information and Communication Technology（信息与通信技术学院）；Faculty of Information Technology（信息技术学院）
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning
VTT Technical Research Centre of Finland（芬兰VTT技术研究中心）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Graduate School of Informatics and Engineering, University of Electro-Communications（电气通信大学信息与工程研究生院）；Auburn University（奥本大学）；National Institute of Informatics（国立信息学研究所）；Centre for Wireless Communications, University of Oulu（奥卢大学无线通信中心）
QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing
Department of Computer Engineering, Sharif University of Technology（计算机工程系，谢赫拉特夫大学）；Department of Electrical Engineering, Sharif University of Technology（电气工程系，谢赫拉特夫大学）
A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
School of Information and Communications Technology, Hanoi University of Science and Technology, Vietnam（河内科学技术大学信息与通信学院）；Faculty of Electrical and Electronic Engineering, Hanoi Open University, Vietnam（河内开放大学电子与电气工程学院）；Posts and Telecommunications Institute of Technology Hanoi, Vietnam（河内邮电技术研究所）；Faculty of Informatics, University of Debrecen, Hungary（德布勒恩大学信息学院）
AoI-Aware Task Offloading and Transmission Optimization for Industrial IoT Networks: A Branching Deep Reinforcement Learning Approach
Laboratory of Future Networks, University of Science and Technology of China（未来网络实验室，中国科学技术大学）；Deep Space Exploration Laboratory（深空探测实验室）；Department of Computing, The Hong Kong Polytechnic University（computing系，香港理工大学）
Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications
VTT Technical Research Centre of Finland（芬兰VTT技术研究中心）；University of Electro-Communications（电气通信大学）；Shizuoka University（静冈大学）；University of Windsor（温莎大学）；National Institute of Informatics（国立信息学研究所）
DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学信息科学与技术国家研究中心电子工程系）；State Key Lab. of ISN and School of Telecommunications Engineering, Xidian University（西安电子科技大学信息与通信系统国家重点实验室和电信工程学院）；Department of Electronic Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；Department of Electrical and Computer Engineering, the Hong Kong University of Science and Technology (HKUST)（香港科技大学电子与计算机工程系）
Joint Channel Estimation and Computation Offloading in Fluid Antenna-assisted MEC Networks
School of Telecommunications Engineering, Xidian University（西电电信工程学院）；Center for Pervasive Communications and Computing, University of California at Irvine（加州大学 Irvine 分中心）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan（教育部计算电力网络与信息安全重点实验室，山东计算机科学中心（济南国家超算中心），齐鲁工业大学（山东省科学院），济南）；Department of Information and Electronic Engineering, Muroran Institute of Technology（Muroran 工业技术学院信息电子工程系）；Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（深圳 MSU-BIT 大学人工智能研究院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；Department of Electrical and Computer Engineering, The University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）；School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
GraphEdge: Dynamic Graph Partition and Task Scheduling for GNNs Computing in Edge Network
Guangxi Key Laboratory of Multimedia Communications and Network Technology（广西多媒体通信与网络技术重点实验室）；School of Computer, Electronics and Information, Guangxi University（广西大学计算机、电子与信息学院）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Pazhou Laboratory（琶洲实验室）；College of Cyber Security, Jinan University（暨南大学网络安全学院）；Department of Information Systems, University of Maryland, Baltimore County (UMBC)（马里兰大学巴尔的摩县信息学院）
