移动通信2026，Vol.50Issue(2)：68-74,7.DOI:10.3969/j.issn.1006-1010.20251130-0001
基于多模态环境感知的车联网数字孪生信道多任务实现
Multi-Task Realization of Digital Twin Channel for Internet of Vehicles Based on Multi-Modal Environmental Sensing
摘要
Abstract
The integration of ubiquitous sensing and artificial intelligence provides a novel paradigm for channel prediction.This paper proposes a multi-task realization framework for digital twin channel utilizing multi-modal environmental sensing,designed for dynamic vehicular network communication scenarios.Leveraging real-time image,point cloud,and location data collected at the vehicle side,the proposed framework constructs a multi-modal feature extraction-fusion network based on deep learning and cross-attention mechanisms,enabling fine-grained extraction and complementary fusion of environmental features for high-accuracy prediction of path loss and optimal beam index.Experimental results demonstrate that the proposed model effectively establishes an accurate mapping from environmental features to channel information.Specifically,under the multi-modal fusion framework,the prediction accuracy for path loss and beam index improves by 1.45 dB and 9.23%,respectively,over single-modal approaches,thereby validating the effectiveness of multi-modal fusion in enhancing channel prediction performance.关键词
数字孪生信道/多模态环境感知/深度学习/交叉注意力机制/车联网通信Key words
digital twin channel/multi-modal environmental sensing/deep learning/cross-attention mechanism/Internet of vehicles communication分类
信息技术与安全科学引用本文复制引用
仇玥龙,于力,张建华,张宇翔,蔡逸辰,王森,赵殊伦..基于多模态环境感知的车联网数字孪生信道多任务实现[J].移动通信,2026,50(2):68-74,7.基金项目
青年科学基金项目"基于传播环境信息表征的分层信道在线智能预测方法及应用",(A类)"无线信道的建模理论与实验研究"(62401084,62525101) （A类）
国家重点研发计划"复杂场景的快速感知与高动态环境的三维自动重建"(2023YFB2904801) （2023YFB2904801）
北京邮电大学-中国移动联合研究院资助项目 （）
