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A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency
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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.
Forward citations
Cited by 7 Pith papers
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Generalizable and Computational Efficient Channel Extrapolation for 6G: A Configurable AI-Driven Framework Built from a Modular Perspective
A three-stage modular AI framework, pretrain, cluster experts, and learn routing, improves channel extrapolation accuracy and cuts FLOPs in simulated 6G scenarios.
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WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model
A multi-modal foundation model pre-trained to align LiDAR/camera observations with radio-channel features improves four physical-layer tasks and transfers to unseen scenarios with frozen backbones.
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WiFo-2: a generalist foundation model unifies heterogeneous wireless system design
WiFo-2 is a space-time-frequency foundation model pretrained on heterogeneous CSI data that delivers strong zero-shot and few-shot performance across wireless communications and sensing tasks.
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IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G
A self-supervised encoder trained on raw multi-antenna I/Q data reaches strong few-shot accuracy on modulation, angle-of-arrival, beam prediction, and RF fingerprinting tasks.
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CSI2Vec: Towards a Universal CSI Feature Representation for Positioning and Channel Charting
A self-supervised neural network, CSI2Vec, maps wireless channel measurements from different environments and hardware into compact spatial codes that support positioning and channel charting.
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Towards channel foundation models (CFMs): Motivations, methodologies and opportunities
A survey and position paper proposing channel foundation models, with experiments on two pretrained CSI models showing gains over a vanilla ViT baseline.
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Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration
The paper proposes a systematic classification and two roadmaps for using foundation models (LLMs and wireless foundation models) to design Synesthesia of Machines systems for 6G, with preliminary case-study evidence ...
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