6 papers · 1 filter
Hybrid Ray-Tracing and Physics-Embedded Neural Modeling for High-Fidelity Channel Reconstruction in Wireless Digital Twins
Huiwen Zhang, Chu Ma, Feng Ye
Many existing wireless digital twins (DTs) rely heavily on ray-tracing (RT)-based channel simulators to achieve scalability in large-scale environments. However, the geometric-opti…
PE-EK-PINN: Physics Embedding with Evolving Kernel for Scalable Physics-Informed Neural Networks
Huiwen Zhang, Feng Ye, Chu Ma
Physics-Informed Neural Networks (PINNs) embed governing equations into deep learning, but enforce them only through loss residuals, leaving highly oscillatory wave behavior to be…
TG-DIN: Theory-Guided Demand Inference Network for Generalizable QoS Prediction
Fuliang Yang, Feng Ye
Network measurements reveal the service a flow receives, not the load it offers to the network. Predictors trained directly on observed traffic therefore become tied to the operati…
Fine-Grained Network Traffic Classification with Contextual QoS Profiling
Huiwen Zhang, Feng Ye
Accurate network traffic classification is vital for managing modern applications with strict Quality of Service (QoS) demands, such as edge computing, real-time XR, and autonomous…
LLM-supported 3D Modeling Tool for Radio Radiance Field Reconstruction
Chengling Xu, Huiwen Zhang, Haijian Sun +1
Accurate channel estimation is essential for massive multiple-input multiple-output (MIMO) technologies in next-generation wireless communications. Recently, the radio radiance fie…
Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
Huiwen Zhang, Feng Ye, Chu Ma
Large-scale wave field reconstruction requires precise solutions but faces challenges with computational efficiency and accuracy. The physics-based numerical methods like Finite El…