Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications

Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications

Exponential growth in data rate demands has driven efforts to develop novel beamforming techniques for realizing massive multiple-input and multiple-output (MIMO) systems in sixth-generation (6G) terabits per second wireless communications. Existing beamforming techniques rely on conventional optimi...

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Bibliographic Details
Main Authors: Tan, Yi Ji, Zhu, Changyan, Tan, Thomas CaiWei, Kumar, Abhishek, Wong, Liang Jie, Chong, Yidong, Singh, Ranjan
Other Authors: School of Physical and Mathematical Sciences
Format: Journal Article
Language:English
Published: 2022
Subjects:
Science::Physics
Deep Reinforcement Learning
Terahertz Beamforming
Terahertz Time-Domain Spectroscopy
Online Access:https://hdl.handle.net/10356/161695

Internet

https://hdl.handle.net/10356/161695

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