MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning

Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intell...

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Vydáno v:	IEEE journal on selected areas in communications Ročník 39; číslo 7; s. 2182 - 2197
Hlavní autoři:	Hu, Jingzhi, Zhang, Hongliang, Bian, Kaigui, Renzo, Marco Di, Han, Zhu, Song, Lingyang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Témata:	Accuracy Algorithms Antennas beamformer pattern design Deep learning deep reinforcement learning Electronics Engineering Sciences Entropy (Information theory) Machine learning Mapping metasurface Metasurfaces Object recognition Optimization policy gradient algorithm Radio frequency RF 3D sensing RF signals Sensors Three-dimensional displays
ISSN:	0733-8716, 1558-0008
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Abstract	Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the size of the metasurface and the target space influence the sensing accuracy.
AbstractList	Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the sizes of the metasurface and the target space influence the sensing accuracy Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the size of the metasurface and the target space influence the sensing accuracy.
Author	Zhang, Hongliang Bian, Kaigui Han, Zhu Renzo, Marco Di Hu, Jingzhi Song, Lingyang
Author_xml	– sequence: 1 givenname: Jingzhi orcidid: 0000-0002-1965-3576 surname: Hu fullname: Hu, Jingzhi email: jingzhi.hu@pku.edu.cn organization: Department of Electronics, Peking University, Beijing, China – sequence: 2 givenname: Hongliang orcidid: 0000-0003-3393-8612 surname: Zhang fullname: Zhang, Hongliang email: hongliang.zhang92@gmail.com organization: Department of Electrical Engineering, Princeton University, Princeton, NJ, USA – sequence: 3 givenname: Kaigui orcidid: 0000-0003-0136-6082 surname: Bian fullname: Bian, Kaigui email: bkg@pku.edu.cn organization: Department of Computer Science, Peking University, Beijing, China – sequence: 4 givenname: Marco Di surname: Renzo fullname: Renzo, Marco Di email: marco.di-renzo@universite-paris-saclay.fr organization: Université Paris-Saclay, CNRS, Centrale-Supélec, Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, France – sequence: 5 givenname: Zhu orcidid: 0000-0002-6606-5822 surname: Han fullname: Han, Zhu email: hanzhu22@gmail.com organization: Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA – sequence: 6 givenname: Lingyang orcidid: 0000-0001-8644-8241 surname: Song fullname: Song, Lingyang email: lingyang.song@pku.edu.cn organization: Department of Electronics, Peking University, Beijing, China
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SubjectTerms	Accuracy Algorithms Antennas beamformer pattern design Deep learning deep reinforcement learning Electronics Engineering Sciences Entropy (Information theory) Machine learning Mapping metasurface Metasurfaces Object recognition Optimization policy gradient algorithm Radio frequency RF 3D sensing RF signals Sensors Three-dimensional displays
Title	MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning
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