基于可编程超表面的Wi-Fi信号调控

双雅; 李力; 王卓; 魏梦麟; 李廉林

doi:10.12000/JR21012

Controllable Manipulation of Wi-Fi Signals Using Tunable Metasurface

DOI: 10.12000/JR21012

SHUANG Ya¹,
LI Li²,
WANG Zhuo^1
,,
WEI Menglin¹,
LI Lianlin^{1
, ,}

1.
State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, Peking University, Beijing 100871, China
2.
Beijing Aerospace Measurement & Control Technology CO., LTD., Beijing 100871, China

Funds: The National Key Research and Development Program of China (2017YFA0700203)

More Information

Author Bio:
SHUANG Ya, was born in 1993 in Shaanxi, China. She received the B.S. degree from Xidian University, Xi’an, China, in 2016. She is currently pursuing the Ph.D. degree with the Department of Electronics, Peking University, Beijing, China. Her current research interests include metasurface-assisted electromagnetic imaging system, communication system based on information metamaterials, and metasurface design.E-mail: 1601111240@pku.edu.cn

WANG Zhuo, was born in 1995. He received his B.Eng and M.Eng degree in electronic and information engineering from the Xidian University. He is currently working toward the Ph.D. degree in Peking University. His research interest is intelligent electromagnetic sensing. E-mail: 2001111254@stu.pku.edu.cn

WEI Menglin, was born in Sep 1995 in Shanxi, China. He received his bachelor’s degree from Department of Electronics, Peking University in 2018. He joined State Key Laboratory of Advanced Optical Communication Systems and Networks as a PhD student. He has interests in microwave imaging and reconfigurable intelligent metasurface.E-mail: wmlpku@pku.edu.cn

LI Lianlin, was born in 1980. He received his Ph.D. degree from the Institute of Electronics, Chinese Academy of Sciences in 2006. He is currently a hundred talented program professor with Peking University. His research interests are super-resolution imaging, microwave imaging, sparse signal processing, and ultrawideband radar systems.E-mail: lianlin.li@pku.edu.cn

Corresponding author: LI Lianlin E-mail: lianlin.li@pku.edu.cn

摘要

摘要: 可编程超表面是由可调谐的单元在二维平面上组成的平面阵列，具有任意、动态操控电磁波波前的能力，是微波领域前沿研究方向之一。尽管目前基于可编程超表面的电磁调控研究已取得丰硕进展，但是现有技术都需要采用专用发射源主动馈电超表面，这不仅增加了实际系统的复杂性和成本，而且在一定程度上限制了这些技术在现实环境中的应用。因此，该文提出了一种利用可编程超表面灵活调控周围Wi-Fi信号的方法，并通过理论和实验证明了其对Wi-Fi信号的优越调控性能。首先，该文首次提出一种基于可编程超表面散射模型的高效优化算法CWGS，该算法可以重新设计可编程超表面散射场的复振幅分布，从而实现指定位置处的Wi-Fi信号显著增强。其次，该文制作了一款工作频率为2.4 GHz的大规模可编程超表面，并基于该超表面实验验证了优化算法应用于Wi-Fi信号增强调控的可行性和有效性。理论和实验结果均证明，可编程超表面可以在多个位置处实现Wi-Fi信号的动态增强。实验结果表明，经可编程超表面调控后的Wi-Fi信号强度提高了23.5 dB。该文提出的方法提高了可编程超表面在实际应用中的可用性和实用性，有望为无线通信、未来智能家居等领域开辟新的道路。
- 商用Wi-Fi 信号 /
- 可编码超表面 /
- 空间能量分配
Abstract: In this paper, we propose the utilization of a programmable metasurface for flexibly manipulating ambient Wi-Fi signals. First, we propose a new and efficient optimization algorithm CWGS (Complex Weighted Gerchberg-Saxton), which is based on an electromagnetic scattering model of the metasurface. The proposed algorithm quickly redesigns the complex amplitude distribution of the Wi-Fi field bounced off the programmable metasurface to enhance the Wi-Fi signals at desired locations significantly. Second, we fabricated a large-scale programmable metasurface that operates at the 2.4 GHz frequency band. We conducted several experiments using the fabricated metasurface to verify the proposed optimization algorithm’s feasibility and effectiveness. Both the theoretical and experimental results show that the programmable metasurface can dynamically boost Wi-Fi signals at multiple locations. Besides, we experimentally verified that using the developed strategy could improve the Wi-Fi signals by 23.5 dB. The results of our work improve the usability and practicality of the programmable metasurface in real-world applications and pave the way for wireless communications, future smart homes, and other applications.
- Commodity Wi-Fi signals /
- Programmable coding metasurface /
- Spatial energy allocation

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Figure 1. Description of programmable metasurface and EM responses

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Figure 2. The iteration process of amplitude and phase of scattering field bounced off metasurface at given locations for three cases when the developed CWGS algorithm is applied

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Figure 3. The measurement setup of near-field scanning

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Figure 4. Results comparison between prediction and measurement

The first row are the coding sequences. The second to fourth rows are the normalized spatial intensity distributions predicted respectively at z=0.984 m, z=1.257 m, and z=1.531 m using the proposed method. The fifth row are the measured normalized spatial intensity distributions at z=1.531 m using the near-field scanning technology

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Figure 5. Practical experimental setup using the commercial Wi-Fi device

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Figure 6. Measured results of focusing commodity Wi-Fi signals at a single position located at Rx2

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Figure 7. Experimental results for flexible and dynamic enhancements of commodity Wi-Fi signals at multiple locations

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Figure 8. The simulation of complicated energy allocation of Wi-Fi signals in a typical indoor environment

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Table 1. The focus efficiency for six different cases

Cases (dB)	Center (%)	Left (%)	Right (%)	Up (%)	Down (%)	Double (%)
–3	25.36	30.73	40.24	28.51	35.27	21.27
–6	37.42	45.19	59.32	41.81	51.96	38.65

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Table 2. Comparing with the existing works dealing with EM waves manipulation using metasurface

Ref.	f₀ (GHz)	Source type	Working mode of metasurface	Optimization goal of algorithm	Working region	The shape of energy allocation	Reconfig-urability	Maximum focusing efficiency
[6]	6.9	Dedicated source	Active	Field’s amplitude pattern	Near-field	Point-like	YES	–
[7]	10	Dedicated source	Active	Field’s amplitude pattern	Near-field	Point-like	NO	57.8%
[8]	5.75	Dedicated source	Active	Field’s amplitude pattern	Near-field	Point-like	YES	–
[9]	10	Dedicated source	Active	Field’s amplitude pattern	Near-field	Point-like	YES	–
[10]	20	Dedicated source	Active	Field’s amplitude pattern	Near-field	Point-like	NO	52.3%
[11]	10	Dedicated source	Active	Field’s amplitude pattern	Near-field	Point-like	NO	–
[12]	5.8	Dedicated source	Active	Field’s amplitude pattern	Near-field	Point-like	NO	–
[31]	14.3	Dedicated source	Active	Field’s amplitude pattern	Far-field	Single beam	YES	–
[23]	5.8	Dedicated source	Active	Field’s amplitude pattern	Near-field	Point-like	NO	–
[24]	2.3	Dedicated source	Active	Field’s amplitude pattern	Far-field	Single beam	YES	–
[25]	10	Dedicated source	Active	Field’s amplitude pattern	Far-field	Single beam	YES
Proposed	2.4	Commodity wireless device	Passive	Field’s complex amplitude pattern	Not limited	Not limited	YES	59.32%

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