A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency

TL;DR

This paper introduces CSI-CLIP, a novel MIMO wireless channel foundation model that leverages self-supervised learning on multi-modal CSI and CIR data, significantly improving performance in positioning, beam management, and channel identification tasks.

Contribution

The paper presents the first MIMO wireless channel foundation model using contrastive learning on CSI and CIR data, enhancing adaptability and feature extraction in wireless communication.

Findings

Reduces positioning error by 22%

Increases beam management accuracy by 1%

Demonstrates superior performance over traditional methods

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.