This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals and dynamically allocates resources for sensing and communication. A Reinforcement Learning (RL) algorithm is derived to learn and provide suitable actions while adjusting for the uncertainty in the AGV's position. We present closed-form expressions for the achievable communication rate and the Cramer-Rao bound (CRB) to determine the required number of Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the needs of both sensing and communication. The proposed algorithm is validated through a millimeter-Wave (mmWave) simulation, demonstrating significant improvements in both control precision and communication efficiency.
Structural & Granger CAUSALITY for IoT Digital Twin
在这篇关于因果关系分析在物联网中应用的基础性文章中，我们建立了从测量的多通道传感器数据（向量时间序列）中估计结构和格兰杰因果关系因子的基本理论和算法。将向量时间序列建模为结构向量自回归（SVAR）模型，利用卡尔曼滤波器和独立分量分析（ICA）方法，估计结构和广义Granger因果因子。估计的因果因素作为一个栅栏图，我们称之为因果数字孪生。因果数字孪生的实际应用表明，美国宇航局预测数据库轴承数据收集。使用因果数字孪生的反事实实验表明。Causal Digital Twin是一个横向解决方案，适用于工业、制造、汽车、消费、建筑和智慧城市等多个行业的各种用例。
PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation
Dept. of Electrical Engineering, Chalmers Univ. of Technology, Sweden（电气工程系，瑞典查尔姆斯理工大学）；Dept. of Microtechnology and Nanoscience, Chalmers Univ. of Technology, Sweden（微电子与纳米科技系，瑞典查尔姆斯理工大学）
