The increasing complexity, dynamism, and heterogeneity of 6G networks demand management systems that can reason proactively and generalize beyond pre-defined cases. In this paper, we propose a modular, knowledge-defined architecture that integrates Digital Twin models with semantic reasoning and zero-shot learning to enable autonomous decision-making for previously unseen network scenarios. Real-time data streams are used to maintain synchronized virtual replicas of the physical network, which also forecast short-term state transitions. These predictions feed into a knowledge plane that constructs and updates a graph-based abstraction of the network, enabling context-aware intent generation via graph neural reasoning. To ensure adaptability without retraining, the management plane performs zero-shot policy matching by semantically embedding candidate intents and selecting suitable pre-learned actions. The selected decisions are translated and enforced through the control plane, while a closed-loop feedback mechanism continuously refines predictions, knowledge, and policies over time. Simulation results confirm that the proposed framework observes notable improvements in policy response time, SLA compliance rate, and intent matching accuracy.
Optical communication is developing rapidly in the directions of hardware resource diversification, transmission system flexibility, and network function virtualization. Its proliferation poses a significant challenge to traditional optical communication management and control systems. Digital twin (DT), a technology that utilizes data, models, and algorithms and integrates multiple disciplines, acts as a bridge between the real and virtual worlds for comprehensive connectivity. In the digital space, virtual models are stablished dynamically to simulate and describe the states, behaviors, and …
The envisioned 6G networks are expected to support extremely high data rates, low-latency, and radically new applications empowered by machine learning. The futuristic 6G networks require a novel framework that can be used to operate, manage, and optimize its underlying services such as ultra-reliable and low-latency communication, and Internet of everything. In recent years, artificial intelligence (AI) has demonstrated significant success in optimizing and designing networks. The AI-enabled traffic orchestration can dynamically organize different network architectures and slices to provide q…
Reconfigurable Intelligent Surface (RIS) has emerged as one of the key technologies for 6G in recent years, which comprise a large number of low-cost passive elements that can smartly interact with the impinging electromagnetic waves for performance enhancement. However, optimally configuring massive number of RIS elements remains a challenge. In this paper, we present a novel digital-twin framework for RIS-assisted wireless networks which we name it Environment-Twin (Env-Twin). The goal of the Env-Twin framework is to enable automation of optimal control at various granularities. In this pape…
Modular Multi-Domain Digital Twin Architecture: Sustainable Intent-Driven 6G Management
University of Applied Sciences and Arts Western Switzerland（西部瑞士应用科学与艺术大学）；Technische Universität Berlin（柏林工业大学）；Poznań University of Technology（波兹南理工大学）；Centrum Wiskunde & Informatica（荷兰数学与计算机科学研究中心）；Eindhoven University of Technology（埃因霍温理工大学）；Centre Tecnològic de Telecomunicacions de Catalunya（加泰罗尼亚电信技术中心）；Telefónica Innovación Digital（西班牙电信数字创新）；Ceragon Networks（萨瑞昂网络）
In this paper, we investigate joint vehicle association and multi-dimensional resource management in a vehicular network assisted by multi-access edge computing (MEC) and unmanned aerial vehicle (UAV). To efficiently manage the available spectrum, computing, and caching resources for the MEC-mounted base station and UAVs, a resource optimization problem is formulated and carried out at a central controller. Considering the overlong solving time of the formulated problem and the sensitive delay requirements of vehicular applications, we transform the optimization problem using reinforcement lea…
This paper presents a novel approach for computing resource management of edge servers in vehicular networks based on digital twins and artificial intelligence (AI). Specifically, we construct two-tier digital twins tailored for vehicular networks to capture networking-related features of vehicles and edge servers. By exploiting such features, we propose a two-stage computing resource allocation scheme. First, the central controller periodically generates reference policies for real-time computing resource allocation according to the network dynamics and service demands captured by digital twi…
Generative AI-Aided QoE Maximization for RIS-Assisted Digital Twin Interaction
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）
The evil twin attack is a major security threat to WLANs. An evil twin is a rogue AP installed by a malicious user to impersonate legitimate APs. It intends to attract victims in order to intercept their credentials, to steal their sensitive information, to eavesdrop on their data, etc. In this paper, we study the security mechanisms of wireless networks and we introduce the different authentication methods, including 802.1X authentication. We show that 802.1X has improved security through the use of digital certificates but does not define any practical technique for the user to check the net…
Digital Twinning of a Pressurized Water Reactor Startup Operation and Partial Computational Offloading in In-network Computing-Assisted Multiaccess Edge Computing
Chonnam National University（全南大学）；Institute for Energy Technology（能源技术研究所）
This paper studies autonomous and AI-assisted control loops (ACLs) in the next generation of wireless networks in the lens of multi-agent environments. We will study the diverse interactions and conflict management among these loops. We propose "interaction and conflict management" (ICM) modules to achieve coherent, consistent and interactions among these ACLs. We introduce three categories of ACLs based on their sizes, their cooperative and competitive behaviors, and their sharing of datasets and models. These categories help to introduce conflict resolution and interaction management mechani…
Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins
Department of Electrical and Computer Engineering, McMaster University, CANADA（电气与计算机工程系，麦斯特大学）；Department of Computing and Software, McMaster University, CANADA（计算与软件系，麦斯特大学）
Digital Twin Enabled Simultaneous Learning and Modeling for UAV-assisted Secure Communications with Eavesdropping Attacks
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；Department of Equipment Management and Unmanned Aerial Vehicle Engineering, Air Force Engineering University（空军工程大学装备管理与无人机工程系）
Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges
State Key Laboratory of ISN（ISN国家重点实验室）；School of Telecommunications Engineering, Xidian University（西安电子科技大学通信工程学院）；School of Cyber Engineering, Xidian University（西安电子科技大学网络与信息安全学院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；Department of Electrical and Computer Engineering, Auburn University（奥本大学电气与计算机工程系）
Prior efforts have shown that network-assisted schemes can improve the Quality-of-Experience (QoE) and QoE fairness when multiple video players compete for bandwidth. However, realizing network-assisted schemes in practice is challenging, as: i) the network has limited visibility into the client players' internal state and actions; ii) players' actions may nullify or negate the network's actions; and iii) the players' objectives might be conflicting. To address these challenges, we formulate network-assisted QoE optimization through a cascade control abstraction. This informs the design of CAN…
