用对比学习建模信道数据，提升多场景通信精度
A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency
- 将信道状态信息与冲激响应视为多模态对，通过对比学习训练
- 定位任务误差降低22%，波束管理准确率提升1%
- 适合研究感知-通信融合的学者，为无线建模提供新范式
在人工智能领域，自监督学习通过大规模无标签数据预训练展现出优异的泛化能力，这对无线通信模型适应多样场景尤为重要。本文创新性地将信道状态信息（CSI）与信道冲激响应（CIR）视为天然对齐的多模态数据，提出首个MIMO无线信道基础模型CSI-CLIP。通过有效捕捉CIR与CSI的联合表示，CSI-CLIP在跨场景适应性和鲁棒特征提取方面表现突出。实验表明，在定位任务中，其均方误差距离减少22%；在波束管理任务中，准确率较传统监督方法提升1%，通道识别任务亦有显著改进。这些成果不仅凸显了CSI-CLIP在感通一体化中的潜力与价值，也展示了其相对于现有技术的优势。此外，将CSI与CIR视为多模态对，并采用对比学习构建无线信道基础模型，为MIMO无线通信领域开辟了新的研究方向。
原文摘要 · Abstract (English)
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.
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