most citedSampling Theory of Jointly Bandlimited Time-vertex Graph Signals

8 citations · 10 across the 6 of their papers we have counts for

collaborators

6 papers

cs.NI2025

A Modular and Scalable Simulator for Connected-UAVs Communication in 5G Networks

Yong Su, Yiyi Chen, Shenghong Yi +4

Cellular-connected UAV systems have enabled a wide range of low-altitude aerial services. However, these systems still face many challenges, such as frequent handovers and the inef…

eess.SP2025

On Sampling of Multiple Correlated Stochastic Signals

Lin Jin, Hang Sheng, Hui Feng +1

Multiple stochastic signals possess inherent statistical correlations, yet conventional sampling methods that process each channel independently result in data redundancy. To lever…

eess.SP20252 cited

Subset Random Sampling and Reconstruction of Finite Time-Vertex Graph Signals

Hang Sheng, Qinji Shu, Hui Feng +1

Finite time-vertex graph signals (FTVGS) provide an efficient representation for capturing spatio-temporal correlations across multiple data sources on irregular structures. Althou…

eess.SP20258 cited

Sampling Theory of Jointly Bandlimited Time-vertex Graph Signals

Hang Sheng, Hui Feng, Junhao Yu +2

Time-vertex graph signal (TVGS) models describe time-varying data with irregular structures. The bandlimitedness in the joint time-vertex Fourier spectral domain reflects smoothnes…

eess.SP2024

Subset Random Sampling of Finite Time-vertex Graph Signals

Hang Sheng, Qinji Shu, Hui Feng +1

Time-varying data with irregular structures can be described by finite time-vertex graph signals (FTVGS), which represent potential temporal and spatial relationships among multipl…

cs.LG2024

The Transferability of Downsamped Sparse Graph Convolutional Networks

Qinji Shu, Hang Sheng, Feng Ji +2

To accelerate the training of graph convolutional networks (GCNs) on real-world large-scale sparse graphs, downsampling methods are commonly employed as a preprocessing step. Howev…