1 citations · 1 across the 7 of their papers we have counts for
7 papers
Parallel Collaborative ADMM Privacy Computing and Adaptive GPU Acceleration for Distributed Edge Networks
Mengchun Xia, Zhicheng Dong, Donghong Cai +3
Distributed computing has been widely applied in distributed edge networks for reducing the processing burden of high-dimensional data centralization, where a high-dimensional comp…
Autonomous Driving with RSMA-Enabled Finite Blocklength Transmissions: Ergodic Performance Analysis and Optimization
Yi Wang, Yingyang Chen, Li Wang +3
Rate-splitting multiple access (RSMA) is a key technology for next-generation multiple access systems due to its robustness against imperfect channel state information (CSI). This…
A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems
Kang Zhou, Weixi Zhou, Donghong Cai +4
In this paper, we consider a novel optimization design for multi-waveguide pinching-antenna systems, aiming to maximize the weighted sum rate (WSR) by jointly optimizing beamformin…
QoS-Aware NOMA Design for Downlink Pinching-Antenna Systems
Yanqing Xu, Zhiguo Ding, Donghong Cai +1
Pinching antennas, implemented by applying small dielectric particles on a waveguide, have emerged as a promising flexible-antenna technology ideal for next-generation wireless com…
Open Set RF Fingerprinting Identification: A Joint Prediction and Siamese Comparison Framework
Donghong Cai, Jiahao Shan, Ning Gao +4
Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio-frequency device by analyzing the si…
Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning
Bibo Wu, Fang Fang, Xianbin Wang +3
Hierarchical federated learning (HFL) shows great advantages over conventional two-layer federated learning (FL) in reducing network overhead and interaction latency while still re…
MIM-GAN-based Anomaly Detection for Multivariate Time Series Data
Shan Lu, Zhicheng Dong, Donghong Cai +2
The loss function of Generative adversarial network(GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper…