Incentive-based task offloading for digital twins in 6G native artificial intelligence networks:a learning approach  

作　　者：Tianjiao CHEN Xiaoyun WANG Meihui HUA Qinqin TANG 

机构地区：[1]China Mobile Research Institute,Beijing 100053,China [2]ZGC Institute of Ubiquitous-X Innovation and Applications,Beijing 100080,China [3]China Mobile Communications Group Corporation,Beijing 100032,China [4]School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2025年第2期214-229,共16页信息与电子工程前沿(英文版)

基　　金：supported by the National Key R&D Program of China(No.2022YFB2902100)。

摘　　要：A communication network can natively provide artificial intelligence(AI)training services for resourcelimited network entities to quickly build accurate digital twins and achieve high-level network autonomy.Considering that network entities that require digital twins and those that provide AI services may belong to different operators,incentive mechanisms are needed to maximize the utility of both.In this paper,we establish a Stackelberg game to model AI training task offloading for digital twins in native AI networks with the operator with base stations as the leader and resource-limited network entities as the followers.We analyze the Stackelberg equilibrium to obtain equilibrium solutions.Considering the time-varying wireless network environment,we further design a deep reinforcement learning algorithm to achieve dynamic pricing and task offloading.Finally,extensive simulations are conducted to verify the effectiveness of our proposal.

关 键 词：Digital twin network Native artificial intelligence Stackelberg game Task offloading Deep reinforcement learning 

分 类 号：TN929.5[电子电信—通信与信息系统]