5 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…
Environment-Conditioned Diffusion Meta-Learning for Data-Efficient WiFi Localization
Jun Gao, Zheng Xing, Wenliang Lin +5
Fingerprinting-based localization often suffers from poor cross-environment generalization, especially when only a few labeled samples are available in the target environment. Exis…
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…