Diffusion-Enabled Digital Twin Synchronization for AIGC Services in Space–Air–Ground-Integrated Networks

作     者：Li, Ya Jin, Kebing Tang, Jianhang Zhang, Yang Feng, Yixiong 

作者机构：Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China 

出 版 物：《IEEE Internet of Things Journal》 (IEEE Internet Things J.)

年 卷 期：2025年第12卷第9期

页      面：12989-13004页

核心收录：

学科分类：0810[工学-信息与通信工程] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0813[工学-建筑学] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0701[理学-数学] 0811[工学-控制科学与工程] 0823[工学-交通运输工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported in part by the National Natural Science Foundation of China under Grant 52130501, Grant 62071343, and Grant 62466009 in part by the Guizhou Provincial Basic Research Program (Natural Science) under Grant ZKYiBan048 in part by the Youth Science and Technology Talent Growth Project of Guizhou Education Department under Grant QianjiaoJi22 in part by the Guizhou Science and Technology Plan Project under Grant QKHPT-KXJZ002 in part by the Foundation of State Key Laboratory of Public Big Data under Grant PBD2023-12 in part by the Collaborative Innovation Center of Novel Software Technology and Industrialization and in part by the Key Research and Development Program of Zhejiang Province under Grant 2023C01214 

主　　题：Resource allocation 

摘      要：Artificial intelligence-generated content (AIGC) is increasingly featuring a key to extract intent information from external instructions and generate required content in digital twin (DT)-enabled application scenarios. To construct DT contexts as the input of generative artificial intelligence (GenAI) algorithms, space–air–ground integrated networks (SAGINs) with hierarchical structures can facilitate object cloning from the physical world to a virtual space within vast geographical regions. In this work, we propose a novel DT synchronization framework residing in SAGINs to provide AIGC services. Autonomous aerial vehicles (AAVs) are in charge of gathering real-time information from the external environment and transmitting synchronization data to the core cloud via a communication relay, i.e., base station (BS) or satellite. In the proposed framework, we develop a resource allocation problem for DT synchronization, aiming to minimize the time-average energy costs of AAVs under the constraints on resource provision and long-term transmission queue stability. To address the complexity and dynamics of SAGINs, we first transform the original resource allocation problem into several deterministic problems based on the Lyapunov optimization. Then, a diffusion model-based resource allocation (DRA) algorithm is developed to solve the deterministic problem in each time slot, where a novel diffusion model is proposed to generate integer relay selection decisions with the aid of auxiliary gradients provided by conventional model-based optimization. Finally, we provide theoretical and simulation evaluations to demonstrate that the DRA algorithm can reduce energy consumption and improve resource utilization by comparing it with deep reinforcement learning (DRL) and heuristic algorithms. © 2014 IEEE.