51. Deep Learning based Beamforming for FD-MIMO Downlink Transmission : (Invited Paper)
- Author
-
Xi Yang, Shi Jin, Xiaoxiang Yu, and Xiao Li
- Subjects
Beamforming ,Artificial neural network ,Computer science ,business.industry ,Deep learning ,MIMO ,Data_CODINGANDINFORMATIONTHEORY ,Upper and lower bounds ,Base station ,Channel state information ,Computer Science::Networking and Internet Architecture ,Artificial intelligence ,InformationSystems_MISCELLANEOUS ,business ,Algorithm ,Computer Science::Information Theory - Abstract
In this paper, we investigate the fast downlink beamforming for full-dimension multiple-input multiple-output systems under correlated Rician channels. Under the assumption that the base station (BS) has only statistical channel state information (CSI), we decouple each user’s beamforming vector and derive their optimal beamforming vector through the maximization of the average signal-to-leakage-plus-noise ratio (SLNR) lower bound. Then, to reduce the computation time, a model-driven deep learning (DL)-based beamforming algorithm is proposed, as well as a data-driven algoriothm for comparison. In the model-driven DL-based beamforming algorithm, the process of obtaining the beamforming vector is separated into two parallel neural networks which are constructed and trained independently. The proposed algorithms can achieve similar ergodic rate as the optimal beamforming algorithm with much less computation time, and the model-driven algorithm requires less computing resource than the data-driven algorithm.
- Published
- 2019