Journal
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS
Volume 10, Issue 1, Pages 114-125Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JETCAS.2020.2972764
Keywords
Programmable metasurface; space-time-modulation; deep learning; deep convolutional neural network; complex beamforming
Categories
Funding
- National Science Foundation of China [61571264, 61971263]
- National Key R&D Program of China [2018YFC0603604]
- Guangzhou Science and Technology Plan [201804010266]
- Beijing Innovation Center for Future Chip
- Research Institute of Tsinghua, Pearl River Delta
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Programmable metasurfaces have recently been proposed to dynamically manipulate electromagnetic (EM) waves in both temporal and spatial dimensions. With active components integrated into unit cells of the metasurface, states of the unit cells can be adjusted by digital codes. The metasurface can then construct complex spatial and temporal electromagnetic beams. Given the main parameters of the beam, the optimal codes can be computed by nonlinear optimization algorithms, such as genetic algorithm, particle swarm optimization, etc. The high computational complexity of these algorithms makes it very challenging to compute the codes in real time. In this study, we applied deep learning techniques to compute the codes. A deep convolutional neural network is designed and trained to compute the required element codes in milliseconds, given the requirement of the waveform. The average accuracy of the prediction reaches more than 94 percent. This scheme is validated on a 1-bit programmable metasurface and both experimental and numerical results agree with each other well. This study shows that machines may learn the physics of modulating electromagnetic waves with the help of the good generalization ability in deep convolutional neural networks. The proposed scheme may provide us with a possible solution for real-time complex beamforming in antenna arrays, such as the programmable metasurface.
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