3 citations · 4 across the 6 of their papers we have counts for
8 papers · 1 filter
TiRE-GAN: Task-Incentivized Generative Learning for Radiomap Estimation
Yueling Zhou, Achintha Wijesinghe, Yibo Ma +2
To characterize radio frequency (RF) signal power distribution in wireless communication systems, the radiomap is a useful tool for resource allocation and network management. Usua…
Reinforcement Learning for Robust Header Compression under Model Uncertainty
Shusen Jing, Songyang Zhang, Zhi Ding
Robust header compression (ROHC), critically positioned between the network and the MAC layers, plays an important role in modern wireless communication systems for improving data…
Radiomap Inpainting for Restricted Areas based on Propagation Priority and Depth Map
Songyang Zhang, Tianhang Yu, Brian Choi +2
Providing rich and useful information regarding spectrum activities and propagation channels, radiomaps characterize the detailed distribution of power spectral density (PSD) and a…
Exemplar-Based Radio Map Reconstruction of Missing Areas Using Propagation Priority
Songyang Zhang, Tianhang Yu, Jonathan Tivald +3
Radio map describes network coverage and is a practically important tool for network planning in modern wireless systems. Generally, radio strength measurements are collected to co…
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation
Songyang Zhang, Han Zhang, Shuguang Cui +1
Graph convolutional networks (GCN) have been recently utilized to extract the underlying structures of datasets with some labeled data and high-dimensional features. Existing GCNs…
Hypergraph Spectral Analysis and Processing in 3D Point Cloud
Songyang Zhang, Shuguang Cui, Zhi Ding
Along with increasingly popular virtual reality applications, the three-dimensional (3D) point cloud has become a fundamental data structure to characterize 3D objects and surround…