Multimodal-Wireless Papers

If your research utilizes the Multimodal-Wireless dataset, we warmly welcome you to share your related publications with us.
Please send the paper title, authors, and publication venue (conference/journal/arxiv) to lliang@seu.edu.cn.

Multimodal-Wireless: A Large-Scale Dataset for Sensing and Communication
T. Mao, L. Liang, J. Yang, H. Ye, S. Jin, and G. Y. Li, “Multimodal-Wireless: A Large-Scale Dataset for Sensing and Communication,” in Proc. IEEE Int. Conf. Commun. (ICC), 2026.

Beam Prediction Based on Multimodal Large Language Models
T. Mao, L. Liang, J. Yang, X. Li, S. Jin, and G. Y. Li, “Beam Prediction Based on Multimodal Large Language Models,” arXiv preprint arXiv:2603.15093, 2026.

Improving Channel Estimation via Multimodal Diffusion Models with Flow Matching
X. Fan, X. Zhou, L. Liang, X. Li, S. Jin, “Improving Channel Estimation via Multimodal Diffusion Models with Flow Matching,” in Proc. IEEE Veh. Technol. Conf. (VTC-Fall), 2026.

CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models Benchmark
Y. Gao, W. Yu, J. Jiang, Y. Li, X. Guo, and S. Xu, “CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models,” arXiv preprint arXiv:2607.14975, 2026.

Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approach Localization
C. Zheng, J. He, G. Cai, H. Wymeersch, and M. Debbah, “Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approach,” arXiv preprint arXiv:2609.23372, 2026.