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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal on selected areas in communications Vol. 39; no. 7; pp. 2182 - 2197
Main Authors: Hu, Jingzhi, Zhang, Hongliang, Bian, Kaigui, Renzo, Marco Di, Han, Zhu, Song, Lingyang
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2021.3078492