Fully-Passive Versus Semi-Passive IRS-Enabled Sensing: SNR and CRB Comparison

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Abstract
This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully- and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze both the sensing signal-to-noise ratio (SNR) and the Cramér-Rao bound (CRB) for estimating the target’s direction-of-arrival (DoA) with joint transmit and reflective beamforming optimization. First, we characterize the maximum sensing SNR when the BS-IRS channel follows line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elements N equipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally to N2 and N4 for the semi- and fully-passive IRSs, respectively. Then, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. It is shown that when N grows, the minimum CRB decreases inversely proportionally to N4 and N6 for the semi- and fully-passive IRS, respectively. Finally, numerical results are presented to corroborate our analysis across general channel conditions. It is shown that the fully-passive IRS outperforms the semi-passive counterpart when N exceeds a certain threshold due to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss.
Funding Information
  • National Natural Science Foundation of China (62471424)
  • Basic Research of Hetao Shenzhen-HK S&T Cooperation Zone (HZQB-KCZYZ-2021067)
  • National Natural Science Foundation of China (92267202)
  • Guangdong Provincial Key Laboratory of Future Networks of Intelligence (2022B1212010001)
  • Guangdong Major Project of Basic and Applied Basic Research (2023B0303000001)
  • Xinmin Li was supported by the Natural Science Foundation of Chengdu University (Z3869)
  • Xiaoqi Qin was supported in part by the National Natural Science Foundation of China (62371072)
  • Beijing Nova Program (2024102)

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