2 citations · 3 across the 4 of their papers we have counts for
6 papers
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks
Chenyang Qiu, Guoshun Nan, Tianyu Xiong +6
Graph convolution networks (GCNs) are extensively utilized in various graph tasks to mine knowledge from spatial data. Our study marks the pioneering attempt to quantitatively inve…
SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems
Xinghan Liu, Guoshun Nan, Qimei Cui +6
Recently proliferated semantic communications (SC) aim at effectively transmitting the semantics conveyed by the source and accurately interpreting the meaning at the destination.…
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications
Guoshun Nan, Zhichun Li, Jinli Zhai +7
End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential fo…
Securing Semantic Communications with Physical-layer Semantic Encryption and Obfuscation
Qi Qin, Yankai Rong, Guoshun Nan +4
Deep learning based semantic communication(DLSC) systems have shown great potential of making wireless networks significantly more efficient by only transmitting the semantics of t…
Boosting Physical Layer Black-Box Attacks with Semantic Adversaries in Semantic Communications
Zeju Li, Xinghan Liu, Guoshun Nan +4
End-to-end semantic communication (ESC) system is able to improve communication efficiency by only transmitting the semantics of the input rather than raw bits. Although promising,…
Design and Analysis of Downlink Channel Estimation Based on Parametric Model for Massive MIMO in FDD Systems
Yinsheng Liu, Yinjun Liu, Qimei Cui +1
This paper investigates downlink channel estimation in frequency-division duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems. To reduce the overhead of downli…