7 papers
TG-DIN: Theory-Guided Demand Inference Network for Generalizable QoS Measurement and Prediction
Fuliang Yang, Feng Ye
In this paper, we introduce TG-DIN, a theory-guided demand inference network that infers latent user demand from observable network quality-of-service (QoS) measurements. Rather th…
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…
Terahertz Spatial Wireless Channel Modeling with Radio Radiance Field
John Song, Lihao Zhang, Feng Ye +1
Terahertz (THz) communication is a key enabler for 6G systems, offering ultra-wide bandwidth and unprecedented data rates. However, THz signal propagation differs significantly fro…
Model-based Deep Learning for Wireless Resource Allocation in RSMA Communications Systems
Hanwen Zhang, Mingzhe Chen, Alireza Vahid +2
Rate-splitting multiple access (RSMA) has been proven as an effective communication scheme for 5G and beyond. However, current approaches to RSMA resource management require compli…