Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method
College of Computer Science and Technology, China University of Petroleum (East China)（中国石油大学（华东）计算机科学与技术学院）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Deemed to be University（Thapar工程技术学院计算机科学与工程系（被认定为大学））；Department of Computer Science and Information Engineering, Asia University（亚洲大学计算机科学与信息工程系）
Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the industry. Compared with traditional wireless communication networks, SAGIN is more flexible and reliable, and it has wider coverage and higher quality of seamless connection. However, due to its inherent heterogeneity, time-varying and self-organizing characteristics, the deployment and use of SAGIN still faces huge challenges, among which the orchestration of heterogeneous resources is a key issue. Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN's heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We model the different network segments of SAGIN, and set the network attributes according to the actual situation of SAGIN and user needs. In DRL, the agent is acted by a five-layer policy network. We build a feature matrix based on network attributes extracted from SAGIN and use it as the agent training environment. Through training, the probability of each underlying node being embedded can be derived. In test phase, we complete the embedding process of virtual nodes and links in turn based on this probability. Finally, we verify the effectiveness of the algorithm from both training and testing.
Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Management at Open RAN Edges
High-Performance Networks Group, Smart Internet Lab, Department of Electrical and Electronic Engineering, University of Bristol（高性能网络组、智能互联网实验室、电子与电气工程系、布里斯托尔大学）；Bristol Research and Innovation Laboratory, Toshiba Europe Ltd.（布里斯托尔研究与创新实验室、东芝欧洲有限公司）
本文提出将 ETSI MEC 与 Open RAN 集成的编排框架，针对异步流量、异构泛化和在线探索三大挑战，采用时间序列集成、多智能体与增量学习及迁移学习驱动的仿真部署，并在城市级测试平台上验证其可行性。
A-MADiff: Attention-Guided Multi-Agent DRL with Diffusion Policies for Memory-Aware Task Orchestration in Mobile AIGC Networks
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；City University of Hong Kong（香港城市大学）；School of Information and Communication, Guilin University of Electronic Technology（桂林电子科技大学信息与通信学院）；Anhui Engineering Research Center for Agricultural Product Quality Safety Digital Intelligence, Fuyang Normal University（阜阳师范大学安徽省农产品质量安全数字智能工程研究中心）；School of Physics and Electronic Engineering, Fuyang Normal University（阜阳师范大学物理与电子工程学院）；University of Manitoba（曼尼托巴大学）
Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating
State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学物联网智慧城市建设国家重点实验室）；College of Water Conservancy and Hydropower Engineering, Hohai University（河海大学水利水电学院）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）
Joint AoI and Handover Optimization in Space-Air-Ground Integrated Network
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（教育部符号计算与知识工程重点实验室）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；Department of Electronic and Electrical Engineering, Southern University of Science and Technology（南方科技大学电子与电气工程系）；Department of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电子与计算机工程系）
Deep Reinforcement Learning for Urban Air Quality Management: Multi-Objective Optimization of Pollution Mitigation Booth Placement in Metropolitan Environments
School of Computer Science（计算机科学学院）；Engineering, Vellore Institute of Technology（工程学院，维洛雷理工学院）；Chennai Campus（钦奈校区）；Chennai, Tamil Nadu 600127, India（钦奈，泰米尔纳德邦600127，印度）
AI-Driven Green Cognitive Radio Networks for Sustainable 6G Communication
Liverpool John Moores University（利物浦约翰摩尔斯大学）；University Institute of Computing（计算大学学院）；Chandigarh University（昌迪加尔大学）；Gyancity Research Consultancy（Gyancity研究咨询公司）
Joint Trajectory and Resource Optimization of MEC-Assisted UAVs in Sub-THz Networks: A Resources-based Multi-Agent Proximal Policy Optimization DRL with Attention Mechanism
Sustainable Air-Ground Integrated Coverage Networks: ISCC Architecture, Technologies, and Testbed
State Key Laboratory of Integrated Service Networks, Institute of Information Science, Xidian University（西安电子科技大学综合业务网理论及关键技术国家重点实验室信息科学研究所）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Information and Communication Engineering, Dalian University of Technology（大连理工大学信息与通信工程学院）
