Programmable transmission metasurface scattering control under obstacles based on deep learning
Source abstract
Abstract
The emergence of 5G represents a pivotal step in merging mobile communication networks with the Industrial Internet of Things. Despite the numerous advantages of 5G, the presence of unknown obstacles can adversely affect user signals. Although mitigating signal pressures can be achieved by increasing base station density, it often involves bulky equipment and high costs. To address this, we propose a deep learning-based method for controlling tunable transmissive metasurfaces and validate their scattering control capabilities in the presence of obstacles. By constructing a network model to analyze the mapping relationship between metasurface arrays and far-field scattering, rapid control of scattering characteristics is achieved. AI-driven high-performance tunable metasurfaces exhibit vast potential applications in intelligent communication, offering a universal solution for intelligent control in complex signal environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Metamaterials and Metasurfaces Applications · Antenna Design and Analysis · Advanced Wireless Communication Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University 1 , Hangzhou 310027, China
International Joint Innovation Center, Key Lab. of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang, The Electromagnetics Academy at Zhejiang University, Zhejiang University 2 , Haining 314400, China
China Mobile Group Zhejiang Co., Ltd. 3 , Hangzhou 310051, China
Jinhua Institute of Zhejiang University, Zhejiang University 4 , Jinhua 321099, China
Jiangnan Institute of Mechanical and Electrical Design Research 5 , Guiyang 550000, China
Funding
The Top-Notch Young Talent of Zhejiang Province · Fundamental Research Funds for the Central Universities · Postdoctoral Fellowship Program of CPSF · Natural Science Foundation of Zhejiang Province · National Natural Science Foundation of China
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