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Title: Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications
Authors: Tan, Yi Ji
Zhu, Changyan
Tan, Thomas CaiWei
Kumar, Abhishek
Wong, Liang Jie
Chong, Yidong
Singh, Ranjan
Keywords: Science::Physics
Issue Date: 2022
Source: Tan, Y. J., Zhu, C., Tan, T. C., Kumar, A., Wong, L. J., Chong, Y. & Singh, R. (2022). Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications. Optics Express, 30(15), 27763-27779.
Project: NRF-CRP23-2019-0005
Journal: Optics Express
Abstract: 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 optimization algorithms that are too computationally expensive for real-time applications and require complex digital processing yet to be achieved for phased array antennas at terahertz frequencies. Here, we develop an intelligent and self-adaptive beamforming scheme enabled by deep reinforcement learning, which can predict the spatial phase profiles required to produce arbitrary desired radiation patterns in real-time. Our deep learning model adaptively trains an artificial neural network in real-time by comparing the input and predicted intensity patterns via automatic differentiation of the phase-to-intensity function. As a proof of concept, we experimentally demonstrate two-dimensional beamforming by spatially modulating broadband terahertz waves using silicon metasurfaces designed with the aid of the deep learning model. Our work offers an efficient and robust deep learning model for real-time self-adaptive beamforming to enable multi-user massive MIMO systems for 6G terahertz wireless communications, as well as intelligent metasurfaces for other terahertz applications in imaging and sensing.
ISSN: 1094-4087
DOI: 10.1364/OE.458823
DOI (Related Dataset): 10.21979/N9/WPX1SE
Schools: School of Physical and Mathematical Sciences 
School of Electrical and Electronic Engineering 
Organisations: Agency for Science, Technology and Research
Research Centres: Centre for Disruptive Photonic Technologies (CDPT) 
Rights: © 2022 Optica Publishing Group under the terms of the OSA Open Access Publishing Agreement. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for noncommercial purposes and appropriate attribution is maintained. All other rights are reserved.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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