Intelligent Reflecting Surface Enabled Sensing: Cramer-Rao Bound Optimization

Song, Xianxin1,2; Xu, Jie1,2; Liu, Fan3; Han, Tony Xiao4; Eldar, Yonina C.5

2023

10.1109/TSP.2023.3280715

IEEE TRANSACTIONS ON SIGNAL PROCESSING   影响因子和分区

1053-587X

1941-0476

This article investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target at its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. We consider two types of target models, namely the point and extended targets, for which the AP aims to estimate the targets direction-of-arrival (DoA) and the target response matrix with respect to the IRS, respectively, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cramer-Rao bound (CRB) on the estimation error. Towards this end, we first obtain the CRB expressions in closed form. It is shown that for the point target, the CRB for estimating the DoA depends on both the transmit and reflective beamformers; while for the extended target, the CRB for estimating the target response matrix only depends on the transmit beamformers. Next, we optimize the joint beamforming design to minimize the CRB for the point target via alternating optimization, semi-definite relaxation, and successive convex approximation. We also obtain the optimal transmit beamforming solution in closed form to minimize the CRB for the extended target. Numerical results show that for both cases, the proposed designs based on CRB minimization achieve improved sensing performances than other traditional schemes.

Intelligent reflecting surface non-line-of-sight wireless sensing Cramer-Rao bound joint transmit and reflective beamforming

SCI ; EI

英语

其他

National Natural Science Foundation of China["U2001208","HZQB-KCZYZ-2021067"] ; Hetao Shenzhen-HK Samp;T Cooperation Zone[92267202] ; Shenzhen Fundamental Research Program[JCYJ20210324133405015] ; Guangdong Provincial Key Laboratory of Future Networks of Intelligence[2022B1212010001] ; Shenzhen Science and Technology Program[20220815100308002]

Engineering

Engineering, Electrical & Electronic

WOS:001018661000001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20232314199095

Array processing ; Cramer-Rao bounds ; Direction of arrival ; Mean square error ; Signal to noise ratio ; Wireless sensor networks

Electromagnetic Waves in Relation to Various Structures:711.2 ; Information Theory and Signal Processing:716.1 ; Radio Systems and Equipment:716.3 ; Data Communication, Equipment and Techniques:722.3 ; Mathematical Statistics:922.2

ENGINEERING

Web of Science

1.Chinese Univ Hong Kong Shenzhen, Sch Sci & Engn SSE, Shenzhen 518172, Peoples R China
2.Chinese Univ Hong Kong Shenzhen, Future Network Intelligence Inst FNii, Shenzhen 518172, Peoples R China
3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China
4.Huawei, 2012 Labs, Wireless Technol Lab, Shenzhen 518129, Peoples R China
5.Weizmann Inst Sci, Fac Math & Comp Sci, IL-7610001 Rehovot, Israel

Song, Xianxin,Xu, Jie,Liu, Fan,et al. Intelligent Reflecting Surface Enabled Sensing: Cramer-Rao Bound Optimization[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING,2023,71:2011-2026.

Song, Xianxin,Xu, Jie,Liu, Fan,Han, Tony Xiao,&Eldar, Yonina C..(2023).Intelligent Reflecting Surface Enabled Sensing: Cramer-Rao Bound Optimization.IEEE TRANSACTIONS ON SIGNAL PROCESSING,71,2011-2026.

Song, Xianxin,et al."Intelligent Reflecting Surface Enabled Sensing: Cramer-Rao Bound Optimization".IEEE TRANSACTIONS ON SIGNAL PROCESSING 71(2023):2011-2026.