6 papers
Rethinking Radiomap Blind Prediction with Limited Environment and Configuration Representations
Xiaojie Li, Yu Han, Han Fang +3
Radiomap blind prediction infers radiomaps from observable representations of the propagation environment and base station (BS) configuration without field measurements. These repr…
Radar-Aided Near-Field Beam Prediction via Beam Map Learning for XL-MIMO V2I Communications
Jiali Nie, Yu Han, Yuanhao Cui +3
Near-field beam training in extremely large-scale multiple-input multiple-output (XL-MIMO) vehicle-to-infrastructure (V2I) systems incurs high overhead due to large range-angle cod…
UAV Trajectory and Bandwidth Allocation for Efficient Data Collection in Low-Altitude Intelligent IoT: A Hierarchical DRL Approach
Zhenjia Xu, Xiaoling Zhang, Nan Qi +3
The low-altitude Internet of Things (IoT), supported by unmanned aerial vehicles (UAVs), provides ground sensing networks with advanced real-time monitoring and data collection. 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…
A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction
Xiaojie Li, Zhijie Cai, Nan Qi +5
The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC fo…