Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-Enabled Vehicular Networks

Chang Liu1; Xuemeng Liu2; Shuangyang Li3; Weijie Yuan4; Derrick Wing Kwan Ng1

2022-09

10.23919/JCIN.2022.9906941

Journal of Communications and Information Networks   Impact Factor & Quartile

2509-3312

integrated sensing and communication predictive beamforming deep learning convolutional longshort term neural network vehicular networks

Others

IEEE

1.School of Electrical Engineering and Telecommunications, the University of New South Wales, Sydney, NSW 2052, Australia
2.School of Electrical and Information Engineering, the University of Sydney, Sydney, NSW 2006, Australia
3.Department of Electrical, Electronic and Computer Engineering, the University of Western Australia, Perth WA 6009, Australia
4.Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China

Chang Liu,Xuemeng Liu,Shuangyang Li,et al. Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-Enabled Vehicular Networks[J]. Journal of Communications and Information Networks,2022,7(3):269-277.

Chang Liu,Xuemeng Liu,Shuangyang Li,Weijie Yuan,&Derrick Wing Kwan Ng.(2022).Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-Enabled Vehicular Networks.Journal of Communications and Information Networks,7(3),269-277.

Chang Liu,et al."Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-Enabled Vehicular Networks".Journal of Communications and Information Networks 7.3(2022):269-277.