A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency
This addresses the need for robust wireless communication models that can generalize across different scenarios, though it appears incremental as it applies existing self-supervised learning techniques to a new domain.
The paper tackles the problem of adapting wireless communication models to various scenarios by proposing CSI-CLIP, the first MIMO wireless channel foundation model that treats Channel State Information (CSI) and Channel Impulse Response (CIR) as multi-modal data. Experimental results show it reduces mean error distance by 22% in positioning tasks and increases accuracy by 1% in beam management tasks compared to traditional supervised methods.
In the field of artificial intelligence, self-supervised learning has demonstrated superior generalization capabilities by leveraging large-scale unlabeled datasets for pretraining, which is especially critical for wireless communication models to adapt to a variety of scenarios. This paper innovatively treats Channel State Information (CSI) and Channel Impulse Response (CIR) as naturally aligned multi-modal data and proposes the first MIMO wireless channel foundation model, named CSI-CLIP. By effectively capturing the joint representations of both CIR and CSI, CSI-CLIP exhibits remarkable adaptability across scenarios and robust feature extraction capabilities. Experimental results show that in positioning task, CSI-CLIP reduces the mean error distance by 22%; in beam management task, it increases accuracy by 1% compared to traditional supervised methods, as well as in the channel identification task. These improvements not only highlight the potential and value of CSI-CLIP in integrating sensing and communication but also demonstrate its significant advantages over existing techniques. Moreover, viewing CSI and CIR as multi-modal pairs and contrastive learning for wireless channel foundation model open up new research directions in the domain of MIMO wireless communications.