A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency

2025-02-17

AI Summary

To address the lack of a general-purpose channel representation model in MIMO wireless communications, this paper introduces CSI-CLIP—the first self-supervised foundation model for wireless channels. It innovatively treats time-domain channel impulse responses (CIR) and frequency-domain channel state information (CSI) as naturally aligned multimodal signals, establishing a CIR–CSI cross-modal consistency pretraining paradigm. Leveraging contrastive learning and joint representation alignment, the model enables large-scale, label-free self-supervised pretraining on channel data. Downstream evaluations demonstrate significant improvements: a 22% reduction in localization mean error and a 1% gain in beam management accuracy, alongside strong cross-scenario generalization. This work pioneers the joint modeling of channel sensing and communication, providing a transferable foundational representation framework for wireless intelligence.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Develops MIMO wireless channel foundation model
• Integrates CIR and CSI as multi-modal data
• Enhances positioning and beam management accuracy

Innovation

• Self-supervised learning for MIMO
• CSI-CLIP integrates CIR and CSI
• Contrastive learning enhances wireless models