1 citations · 4 across the 35 of their papers we have counts for
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RMSampler: Sparse Radio Map Reconstruction with Active Sampling and Diffusion Posterior Correction
Nan Cheng, Xiucheng Wang
Reconstructing a radio map (RM) from sparse received-power measurements supports non-cooperative interference monitoring and spectrum management. However,sparse measurements leave…
BeamRMX: Radiation-Pattern-Driven Learning for Generalizable Beam Radio Map Prediction and Beam Management
Yue Zhang, Xiucheng Wang, Wenshuo Chen +1
The evolution toward sixth-generation (6G) wireless networks is driving larger antenna arrays and highly directional multi-beam transmission, making accurate knowledge of beam-depe…
RMWorld: Task-Aware Radio World Models with Value-of-Information Guided Multi-Trial Learning for Multi-UAV Communication Control
Xiucheng Wang, Nan Cheng, Junxi Huan
Reliable multi-UAV communication control depends on predicting which aerial links will serve traffic before measurements are available. Radio world models (radio WMs) make such pla…
RadioVIL: Anomaly-Aware Diffusion Models for Radio Map Inpainting and Zero-Shot Vehicle Localization
Ruixin Zhao, Xiucheng Wang, Qiming Zhang +3
High-precision radio map construction is essential for emerging 6G Integrated Sensing and Communication (ISAC) applications, including digital twins and intelligent transportation.…
RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization
Xiucheng Wang, Junxi Huang, Nan Cheng
Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks. Predi…
PALM: Single-Station Super-Resolved Small-Scale Radio-Map Localization by Path-Atom Matching
Xiucheng Wang, Nan Cheng
Localizing from a single base station is a longstanding goal, since it removes the synchronized anchors that geometric methods require. A radio map (RM) answers a position query fr…
RadioDiff-Inv2: Differentiable Diffusion Inversion under Location Drift from Sparse Noisy Measurements for Radio Map Estimation
Xiucheng Wang, Kailong Wang, Nan Cheng
Radio map (RM) estimation is a key enabler for environment-aware optimization in 6G wireless networks. In practice, RM construction increasingly relies on crowdsourced received sig…
RMPrior: Bridging Propagation Priors and Diffusion Refinement for Efficient Radio Map Construction
Zixuan Guo, Xiucheng Wang, Nan Cheng
Diffusion models achieve high-fidelity radio map construction through iterative denoising, yet their sampling cost limits practicality in dynamic wireless systems where radio maps…
Map2APS: A Physically Grounded Benchmark for Direct Angle Power Spectrum Prediction from Urban Geometry
Junxi Huang, Xiucheng Wang, Nan Cheng +3
Angle power spectrum (APS) characterizes the directional distribution of received signal power and is directly relevant to beam management and MIMO processing. While environment-aw…
Path-Level Radio Map-Aided Fast and Robust Channel Estimation for Pilot-Starved MIMO-OFDM Systems
Xiucheng Wang, Nan Cheng, Yiyan Zhang +2
Accurate channel estimation in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is challenging when the number of pilot symbols…
SateBeam: Beam-Aware Radio Map Estimation With Physics-Consistent Parametric Modeling for Unknown Multiple Satellites
Xiucheng Wang, Nan Cheng, Zhisheng Yin +2
Dense low-Earth-orbit (LEO) networks increasingly reuse spectrum across moving satellites and overlapping beams, making a spatial radio map essential for interference diagnosis and…
Physics-informed line-of-sight learning for scalable deterministic channel modeling
Xiucheng Wang, Junxi Huang, Conghao Zhou +2
Deterministic channel modeling maps a physical environment to its site-specific electromagnetic response. Ray tracing produces complete multi-dimensional channel information but re…
RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction
Xiucheng Wang, Zixuan Guo, Nan Cheng
Radio maps (RMs) provide spatially continuous propagation characterizations essential for 6G network planning, but high-fidelity RM construction remains challenging. Rigorous elect…
BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding
Xiucheng Wang, Yue Zhang, Nan Cheng
Integrating large language models (LLMs) into wireless communication optimization is a promising yet challenging direction. Existing approaches either use LLMs as black-box solvers…
Neural Electromagnetic Fields for High-Resolution Material Parameter Reconstruction
Zhe Chen, Peilin Zheng, Wenshuo Chen +3
Creating functional Digital Twins, simulatable 3D replicas of the real world, is a central challenge in computer vision. Current methods like NeRF produce visually rich but functio…
Learn for Variation: Efficient AAV Trajectory Learning through a Differentiable Wireless World Model
Xiucheng Wang, Zhenye Chen, Nan Cheng +2
Autonomous aerial vehicles (AAVs) enable data collection for sixth-generation Internet-of-Things networks, but their trajectories couple nonlinear wireless rates with long-horizon…
A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness
Xiucheng Wang, Yuhao Pan, Nan Cheng +9
Radio maps (RMs) provide the digital representation of the wireless propagation environment, mapping complex geographical and topological boundary conditions to critical spatial-sp…
RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse
Xiucheng Wang, Peilin Zheng, Honggang Jia +4
Accurate radio map (RM) construction is essential to enabling environment-aware and adaptive wireless communication. However, in future 6G scenarios characterized by high-speed net…