From the 1 of 6 linked papers with an AI index.
2 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
Wideband Precoding for U6G XL-MIMO Systems: Beam Squint Boundaries and Channel Slicing
Zhizheng Lu, Yu Han, Xiaojie Li +2
The unconventionally large aperture of extremely large-scale multiple-input multiple-output (XL-MIMO) arrays, in conjunction with the wider bandwidths in the upper-6 GHz (U6G) freq…
U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach
Xiaojie Li, Yu Han, Zhizheng Lu +2
The upper 6 GHz (U6G) band with XL-MIMO is a key enabler for sixth-generation wireless systems, yet intelligent radiomap prediction for such systems remains challenging. Existing d…
RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks
Xiaojie Li, Songyang Zhang, Hang Li +7
Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-l…