COST CA20120 INTERACT Framework of Artificial Intelligence-Based Channel Modeling

作     者：He, Ruisi Cicco, Nicola D. Ai, Bo Yang, Mi Miao, Yang Boban, Mate 

作者机构：Beijing Jiaotong Univ Beijing Peoples R China Politecn Milan Milan Italy Univ Twente Enschede Netherlands Huawei Technol Duesseldorf GmbH Dusseldorf Germany 

出 版 物：《IEEE WIRELESS COMMUNICATIONS》 (IEEE Wirel. Commun.)

年 卷 期：2025年

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [62431003, 62221001] COST Action [CA20120 INTERACT] 

主　　题：Predictive models Artificial intelligence Data models Computational modeling Accuracy Uncertainty Costs Channel models Atmospheric modeling Numerical models 

摘      要：Accurate channel models are the prerequisite for communication-theoretic investigations as well as system design. Channel modeling generally relies on statistical and deterministic approaches. However, there are still significant limits for the traditional modeling methods in terms of accuracy, generalization ability, and computational complexity. The fundamental reason is that establishing a quantified and accurate mapping between the physical environment and channel characteristics becomes increasingly challenging for modern communication systems. Here, in the context of COST CA20120 Action, we evaluate and discuss the feasibility and implementation of using artificial intelligence (AI) for channel modeling, and explore where the future of this field lies. Firstly, we present a framework of AI-based channel modeling to characterize complex wireless channels. Then, we highlight in detail some major challenges and present the possible solutions: estimating the uncertainty of AI-based channel predictions;integrating prior knowledge of propagation to improve generalization capabilities;and interpretable AI for channel modeling. We present and discuss numerical results to showcase the capabilities of AI-based channel modeling.