5 papers
Generative Model-Aided Continual Learning for CSI Feedback in FDD mMIMO-OFDM Systems
Guijun Liu, Yuwen Cao, Tomoaki Ohtsuki +2
Deep autoencoder (DAE) frameworks have demonstrated their effectiveness in reducing channel state information (CSI) feedback overhead in massive multiple-input multiple-output (mMI…
A Deep Transfer Learning-Based Low-overhead Beam Prediction in Vehicle Communications
Zhiqiang Xiao, Yuwen Cao, Mondher Bouazizi +2
Existing transfer learning-based beam prediction approaches primarily rely on simple fine-tuning. When there is a significant difference in data distribution between the target dom…
Distributed Gossip-GAN for Low-overhead CSI Feedback Training in FDD mMIMO-OFDM Systems
Yuwen Cao, Guijun Liu, Tomoaki Ohtsuki +2
The deep autoencoder (DAE) framework has turned out to be efficient in reducing the channel state information (CSI) feedback overhead in massive multiple-input multipleoutput (mMIM…
Coverage and Rate Performance Analysis of Multi-RIS-Assisted Dual-Hop mmWave Networks
Yuwen Cao, Xiaowen Wu, Jiguang He +2
Millimeter-wave (mmWave) communication, which operates at high frequencies, has gained extensive research interest due to its significantly wide spectrum and short wavelengths. How…
A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
Yiwei Dong, Shaoxin Ye, Yuwen Cao +3
Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this…