A Study on Multimodal Large Models for Wireless Channels

The rapid evolution of Fifth-Generation (5G) and Sixth-Generation (6G) communication technologies, coupled with complex and dynamic wireless propagation environments and diverse application scenarios, has imposed increasingly stringent demands on channel modeling. Traditional approaches, which predominantly rely on limited measurements or a restricted set of environmental parameters, often fail to comprehensively capture channel variations caused by the superposition of factors in real world such as diverse building shapes and terrain. This inherent limitation constrains the generalization capability and prediction accuracy of such models. In response, Multi-Modal Large Models (MMLMs) have emerged as a prominent research focus in wireless channel modeling nowadays. By integrating multi-source environmental data—such as visual, radar, and sensor information, MMLMs can more comprehensively characterize propagation environments, uncover correlations between different modalities, and enhance modeling accuracy and generalization as well. Hence, this paper will systematically review the fundamental methodologies and practical challenges of traditional wireless channel modeling. It analyzes the advantages of MMLMs in terms of input information richness, multi-perspective environmental comprehension, spatiotemporal sequence modeling, and generalization in complex scenarios, while also comparing their similarities and differences with conventional approaches. Furthermore, the paper synthesizes recent advancements in the application of MMLMs to channel modeling, evaluates their practical utility and developmental prospects, and discusses key implementation challenges along with potential future research directions.