From the 1 of 17 linked papers with an AI index.
17 papers
Radar-Aided Near-Field Beam Prediction via Beam Map Learning for XL-MIMO V2I Communications
Jiali Nie, Yu Han, Yuanhao Cui +3
The paper introduces a passive radar‑aided framework that predicts near‑field beams for XL‑MIMO vehicle‑to‑infrastructure links by learning a mapping from radar Bartlett spectra to…
Low-Complexity Channel Estimation Framework for Non-Square UPA-Assisted XL-MIMO Systems
Yilong Liu, Xi Yang, Binggui Zhou +3
Low-complexity channel state information acquisition is crucial for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. However, practical deployments of non-sq…
XL-ChannelDiff: An Efficient Diffusion-Based Multi-Domain Near-Field Channel Extrapolation Framework for XL-MIMO Systems
Mengyuan Li, Yu Han, Hao Xu +3
Accurate channel state information (CSI) acquisition is essential for unleashing the performance gains of extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Ho…
Digital Twin-Based Channel Generation Toolchain and Foundation Model for Low-Altitude XL-MIMO
Mengyuan Li, Yu Han, Jiachen Tian +2
The rapid development of the low-altitude economy (LAE) has created growing demand for reliable aerial communication systems. Extremely large-scale multiple-input multiple-output (…
Vision-Based Efficient Joint Trajectory and Channel Tracking in Near-Field XL-MIMO Systems
Mengyuan Li, Yu Han, Hao Xu +3
Accurate joint tracking of mobile users, surrounding scatterers, and dynamic channels is a critical task for sixth-generation (6G) wireless systems, essential for both ensuring hig…
NF-TrackLLM: Joint Prediction of UAV Trajectory and Near-Field Beam for LAE XL-MIMO Systems
Qianfan Lu, Mengyuan Li, Jiachen Tian +3
User localization and beam management are tightly linked in extremely large-scale multiple-input multiple-output (XL-MIMO) systems, especially in dense low-altitude economy (LAE) s…