In existing wireless networks, the control programs have been designed manually and for certain predefined scenarios. This process is complicated and error-prone, and the resulting control programs are not resilient to disruptive changes. Data-driven control based on Artificial Intelligence and Machine Learning (AI/ML) has been envisioned as a key technique to automate the modeling, optimization and control of complex wireless systems. However, existing AI/ML techniques rely on sufficient well-labeled data and may suffer from slow convergence and poor generalizability. In this article, focusing on digital twin-assisted wireless unmanned aerial vehicle (UAV) systems, we provide a survey of emerging techniques that can enable fast-converging data-driven control of wireless systems with enhanced generalization capability to new environments. These include SLAM-based sensing and network softwarization for digital twin construction, robust reinforcement learning and system identification for domain adaptation, and testing facility sharing and federation. The corresponding research opportunities are also discussed.
Artificial intelligence (AI)-driven zero-touch network slicing (NS) is a new paradigm enabling the automation of resource management and orchestration (MANO) in multi-tenant beyond 5G (B5G) networks. In this paper, we tackle the problem of cloud-RAN (C-RAN) joint slice admission control and resource allocation by first formulating it as a Markov decision process (MDP). We then invoke an advanced continuous deep reinforcement learning (DRL) method called twin delayed deep deterministic policy gradient (TD3) to solve it. In this intent, we introduce a multi-objective approach to make the central…
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）
Old Dominion University（欧道明大学）；Kyung Hee University（庆熙大学）；Virginia Tech（弗吉尼亚理工学院暨州立大学）
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin …
Collection: UAV-Based Wireless Multi-modal Measurements from AERPAW Autonomous Data Mule (AADM) Challenge in Digital Twin and Real-World Environments
North Carolina State University（北卡罗来纳州立大学）；Southern Methodist University（南方卫理公会大学）；National University of Sciences and Technology（国立科学与技术大学）；University of Utah（犹他大学）；Florida International University（佛罗里达国际大学）；Columbia University（哥伦比亚大学）；The University of Georgia（佐治亚大学）；Florida Atlantic University（佛罗里达大西洋大学）；The University of North Texas（北德克萨斯大学）；University of Minnesota（明尼苏达大学）
TITAN: Twin-Informed Topology Adaptation for LAWN-enabled D2C Communication
Istanbul Technical University（伊斯坦布尔理工大学）；The Mathworks Inc.（MathWorks公司）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；ITU-BTS Digital Twin Application and Research Center（ITU-BTS数字孪生应用与研究中心）；NVIDIA Corporation（英伟达公司）
To harness the full potential of beyond 5G (B5G) communication systems, zero-touch network slicing (NS) is viewed as a promising fully-automated management and orchestration (MANO) system. This paper proposes a novel knowledge plane (KP)-based MANO framework that accommodates and exploits recent NS technologies and is termed KB5G. Specifically, we deliberate on algorithmic innovation and artificial intelligence (AI) in KB5G. We invoke a continuous model-free deep reinforcement learning (DRL) method to minimize energy consumption and virtual network function (VNF) instantiation cost. We present…
Artificial intelligence (AI)-driven zero-touch network slicing is envisaged as a promising cutting-edge technology to harness the full potential of heterogeneous 5G and beyond 5G (B5G) communication systems and enable the automation of demand-aware resource management and orchestration (MANO). In this paper, we tackle the issue of B5G radio access network (RAN) joint slice admission control and resource allocation according to proposed slice-enabling cell-free massive multiple-input multiple-output (mMIMO) setup by invoking a continuous deep reinforcement learning (DRL) method. We present a no…
Un-manned aerial vehicle (UAVs) have the potential to change the landscape of wide-area wireless connectivity by bringing them to areas where connectivity was sparing or non-existent (e.g. rural areas) or has been compromised due to disasters. While Google's Project Loon and Facebook's Project Aquila are examples of high-altitude, long-endurance UAV-based connectivity efforts in this direction, the telecom operators (e.g. AT&T and Verizon) have been exploring low-altitude UAV-based LTE solutions for on-demand deployments. Understandably, these projects are in their early stages and face formid…
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）
Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model
Guilin University of Electronic Technology（桂林电子科技大学）；Guangxi University（广西大学）；National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology (Guangxi)（综合交通大数据应用技术国家工程实验室（广西））；Guangdong University of Technology（广东工业大学）
A UAV-assisted Wireless Localization Challenge on AERPAW
School of Electrical and Computer Engineering, University of Georgia（佐治亚大学电气与计算机工程学院）；University of North Texas（北得克萨斯大学）；NYU Tandon School of Engineering（纽约大学坦登工程学院）；Delmont Systems LLC（德尔蒙特系统有限责任公司）；North Carolina State University（北卡罗来纳州立大学）
"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems
Department of Electrical Engineering and Computer Science, South Dakota State University（南达科他州立大学电气工程与计算机科学系）；School of Computer Science, University of Technology Sydney（悉尼大学计算机科学学院）
本文综述“X of Information”连续体，提出时间、质量/效用、可靠性/鲁棒性及网络/通信四维分类框架，分析AI优化方法与六大应用领域及挑战。
