74 citations · 182 across the 17 of their papers we have counts for
17 papers
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…
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning
Achintha Wijesinghe, Songyang Zhang, Zhi Ding
Recent advances of generative learning models are accompanied by the growing interest in federated learning (FL) based on generative adversarial network (GAN) models. In the contex…
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data
Achintha Wijesinghe, Songyang Zhang, Siyu Qi +1
To satisfy the broad applications and insatiable hunger for deploying low latency multimedia data classification and data privacy in a cloud-based setting, federated learning (FL)…
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…
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy
Achintha Wijesinghe, Songyang Zhang, Zhi Ding
Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation owing to its strong potential in capturing underlying data statistics while preser…
RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network
Songyang Zhang, Achintha Wijesinghe, Zhi Ding
Outdoor radio map estimation is an important tool for network planning and resource management in modern Internet of Things (IoT) and cellular systems. Radio map describes spatial…
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…
Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks
Songyang Zhang, Qinwen Deng, Zhi Ding
Hyperspectral imaging is an important sensing technology with broad applications and impact in areas including environmental science, weather, and geo/space exploration. One import…
Image Processing via Multilayer Graph Spectra
Songyang Zhang, Qinwen Deng, Zhi Ding
Graph signal processing (GSP) has become an important tool in image processing because of its ability to reveal underlying data structures. Many real-life multimedia datasets, howe…
Signal Processing over Multilayer Graphs: Theoretical Foundations and Practical Applications
Songyang Zhang, Qinwen Deng, Zhi Ding
Signal processing over single-layer graphs has become a mainstream tool owing to its power in revealing obscure underlying structures within data signals. However, many real-life d…
An Efficient Hypergraph Approach to Robust Point Cloud Resampling
Qinwen Deng, Songyang Zhang, Zhi Ding
Efficient processing and feature extraction of largescale point clouds are important in related computer vision and cyber-physical systems. This work investigates point cloud resam…
Point Cloud Resampling Through Hypergraph Signal Processing
Qinwen Deng, Songyang Zhang, Zhi Ding
Three-dimensional (3D) point clouds are important data representations in visualization applications. The rapidly growing utility and popularity of point cloud processing strongly…
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…
Point Cloud Segmentation based on Hypergraph Spectral Clustering
Songyang Zhang, Shuguang Cui, Zhi Ding
Hypergraph spectral analysis has emerged as an effective tool processing complex data structures in data analysis. The surface of a three-dimensional (3D) point cloud and the multi…
Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications
Songyang Zhang, Zhi Ding, Shuguang Cui
Signal processing over graphs has recently attracted significant attentions for dealing with structured data. Normal graphs, however, only model pairwise relationships between node…