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From the 1 of 9 linked papers with an AI index.

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eess.SP2026

CSI-CLIP++: A Scalable Channel Foundation Model for Wireless Communication via CIR-CSI Consistency

Jun Jiang, Wenjun Yu, Yunfan Li +2

Self-supervised learning can exploit large-scale unlabeled channel data to improve the transferability of wireless AI models. Existing channel foundation models are often built on…

eess.SP2026

Towards channel foundation models (CFMs): Motivations, methodologies and opportunities

Jun Jiang, Yuan Gao, Xinyi Wu +1

Artificial intelligence (AI) has emerged as a pivotal enabler for next-generation wireless communication systems. However, conventional AI-based models encounter several limitation…

eess.SP2026

Dynamic Channel Charting: An LSTM-AE-based Approach

Yuan Gao, Wenjing Xie, Yiming Liu +3

With the development of the sixth-generation (6G) communication system, Channel State Information (CSI) plays a crucial role in improving network performance. Traditional Channel C…

eess.SP2025

C2S-AE: CSI to Sensing enabled by an Auto-Encoder-based Framework

Jun Jiang, Shugong Xu, Wenjun Yu +1

Next-generation mobile networks are set to utilize integrated sensing and communication (ISAC) as a critical technology, providing significant support for sectors like the industri…

eess.SP2025

A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency

Jun Jiang, Wenjun Yu, Yunfan Li +2

In the field of artificial intelligence, self-supervised learning has demonstrated superior generalization capabilities by leveraging large-scale unlabeled datasets for pretraining…

eess.SP2025

MTCA: Multi-Task Channel Analysis for Wireless Communication

Jun Jiang, Wenjun Yu, Yuan Gao +1

In modern wireless communication systems, the effective processing of Channel State Information (CSI) is crucial for enhancing communication quality and reliability. However, curre…