信道大模型使能的6G环境智能通信及平台实现OA

With the evolution of 6G mobile communications towards integrated sensing and communication as well as ubiquitous connectivity,channel prediction needs to meet the requirements of high dynamism and high precision.However,traditional statistical model paradigms cannot capture the spatiotemporal variations of real propagation environments,and the existing small AI models are poor in multimodal environmental understanding and cross-scenario generalization.To address these challenges,a 6G environment-intelligent communication framework and verification platform enabled by channel large model(ChannelLM)are proposed.Specifically,a multimodal sensing platform is adopted to construct an environment-channel joint dataset and an environmental reconstruction process based on multimodal sensors is designed via combining static mapping and dynamic recognition.Based on this dataset,the proposed ChannelLM is trained to integrate real-time dynamic environmental information for multi-task online channel prediction,enabling a novel paradigm of environment-intelligent communications.Furthermore,a ChannelLM-enabled 6G environment-intelligent communication testbed is developed.Experimental results demonstrate that the platform completes the full pipeline from environmental sensing to channel prediction within a latency of only a few hundred milliseconds while delivering high-accuracy predictions.The results provide essential support for 6G networks to realize environment-adaptive communications,intelligent resource scheduling,and real-time decision-making.