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 beam-forming 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. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

The exponential growth in data rate demands has led to the development of novel beam-forming techniques for massive MIMO systems in 6G terahertz wireless communications. In this study, we propose an intelligent and self-adaptive beamforming scheme enabled by deep reinforcement learning, which can predict the required spatial phase profiles in real-time. Our experimental results demonstrate the feasibility of this approach for two-dimensional beamforming using silicon metasurfaces.