Intelligent Reflecting Surface Aided Wireless Sensing: Applications and Design Issues

Shao X, You C, Zhang R (2024)


Publication Type: Journal article

Publication year: 2024

Journal

Book Volume: 31

Pages Range: 383-389

Journal Issue: 3

DOI: 10.1109/MWC.004.2300058

Abstract

Intelligent reflecting surface (IRS) is an emerging technology that is able to significantly improve the performance of wireless communications by smartly tuning signal reflections at a large number of passive reflecting elements. On the other hand, with ubiquitous wireless devices and ambient radio-frequency signals, wireless sensing has become a promising new application for the next-generation/6G wireless networks. This article proposes a new IRS-aided sensing paradigm for enhancing the performance of wireless sensing cost-effectively by synergizing low-cost IRS and fertile wireless sensing applications. First, we provide an overview of wireless sensing applications and the new opportunities of utilizing IRS for overcoming their performance limitations in practical scenarios. Next, we discuss IRS-aided sensing schemes based on three approaches, namely, passive sensing, semi-passive sensing, and active sensing. We compare their pros and cons in terms of performance, hardware cost, and implementation complexity, and outline their main design issues including IRS deployment, channel acquisition, and reflection design, as well as sensing algorithms. Finally, numerical results are presented to demonstrate the great potential of IRS for improving wireless sensing accuracy and the superior performance of IRS active sensing compared to other schemes.

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How to cite

APA:

Shao, X., You, C., & Zhang, R. (2024). Intelligent Reflecting Surface Aided Wireless Sensing: Applications and Design Issues. IEEE Wireless Communications, 31(3), 383-389. https://doi.org/10.1109/MWC.004.2300058

MLA:

Shao, Xiaodan, Changsheng You, and Rui Zhang. "Intelligent Reflecting Surface Aided Wireless Sensing: Applications and Design Issues." IEEE Wireless Communications 31.3 (2024): 383-389.

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