1 citations · 1 across the 4 of their papers we have counts for
6 papers
Learning Graph Beliefs for Active Radio Mapping With Decision-Certified Multi-Step Sensing
Wangqian Chen, Hao Wang, Yumeng Zhang +3
Radio maps provide essential wireless propagation knowledge for network optimization and autonomous platforms. Acquiring dense measurements, however, is prohibitively costly, requi…
GPARA: Graph-Posterior-Aligned Refinement and Active Acquisition for Grounding Diffusion Priors
Wangqian Chen, Hao Wang, Yumeng Zhang +3
Active grounding of a frozen diffusion prior requires jointly determining where new measurements should be taken and how they should be used to refine the current reconstruction. P…
Geometry-Aligned Differential Privacy for Location-Safe Federated Radio Map Construction
Jijia Tian, Wangqian Chen, Junting Chen +1
Radio maps that describe spatial variations in wireless signal strength are widely used to optimize networks and support aerial platforms. Their construction requires location-labe…
Self-Localizing MIMO Beam Mapping with Continuously Evolving Channel Memory
Wangqian Chen, Junting Chen, Shuguang Cui
Machine learning has greatly advanced data-driven channel modeling and resource optimization. However, most existing methods require accurately location-labeled datasets, which are…
Physics-Informed Neural Networks for MIMO Beam Map and Environment Reconstruction
Wangqian Chen, Junting Chen, Shuguang Cui
As communication networks evolve towards greater complexity (e.g., 6G and beyond), a deep understanding of the wireless environment becomes increasingly crucial. When explicit know…
Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning
Wangqian Chen, Junting Chen
Machine learning (ML) facilitates rapid channel modeling for 5G and beyond wireless communication systems. Many existing ML techniques utilize a city map to construct the radio map…