无线电工程2025，Vol.55Issue(4)：679-686,8.DOI:10.3969/j.issn.1003-3106.2025.04.001
信道通用预训练大模型赋能数字孪生信道:原理与实践
Channel General Pre-trained Large Model Empowering Digital Twin Channel:Theory and Practice
摘要
Abstract
The future 6G aims to provide ultra-reliable,intelligent network connectivity and realize the internet of everything in dynamic environments.Digital Twin Channel(DTC),as a key technology supporting the intelligence of 6G network,constructs high-fidelity twin channels online in the digital world to assist with proactive network adaptation and precise decision-making.However,traditional AI small models are usually tailored to specific tasks or scenarios.These models face significant challenges in predicting complex,and highly dynamic wireless channels of 6G,which struggle to fully meet the requirements of DTC.Large Language Model(LLM),with its powerful capabilities in multimodal feature fusion,high-dimensional data modeling and so on,have the potential to overcome these challenges.The DTC framework is integrated with LLM for the first time in this research,proposing a channel general pre-trained large model(ChannelGPT)to tackle the substantial challenges of 6G wireless channels.Specifically,ChannelGPT is designed with three core layers.The data processing layer constructs large-scale environment and channel datasets.The algorithm model layer fully explores the mapping between wireless environment information and channel characteristics.The functional application layer is expected to support multi-task joint optimization.Simulation results demonstrate that ChannelGPT outperforms small models in prediction accuracy and multimodal fusion capabilities,providing a promising approach for realizing DTC.关键词
大语言模型/信道预测/数字孪生信道/深度学习Key words
LLM/channel prediction/DTC/deep learning分类
电子信息工程引用本文复制引用
张建华,史廉正,于力,黎明月,田磊,王启星,刘光毅..信道通用预训练大模型赋能数字孪生信道:原理与实践[J].无线电工程,2025,55(4):679-686,8.基金项目
国家重点研发计划(2023YFB2904805) （2023YFB2904805）
国家自然科学基金(62401084) National Key R&D Program of China(2023YFB2904805) （62401084）
National Natural Science Foundation of China(62401084) （62401084）
