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Semi-Passive IRS Enabled Sensing with Group Movable Sensors

Qiaoyan Peng, Qingqing Wu, Wen Chen, Guangji Chen, Ying Gao, Lexi Xu, Shaodan Ma

TL;DR

This work tackles accurate DoA estimation in a semi-passive IRS sensing system by optimizing the positions of group-movable sensors to minimize the Cramer-Rao bound (CRB). The authors derive the DoA CRB ${\mathrm{CRB}}_{\theta}(\boldsymbol{x})$, show it scales inversely with ${\rm var}(\boldsymbol{x})$, and obtain a closed-form optimal sensor-position solution under grouping constraints. They analyze how the number of groups $L$ and the available budget affect the CRB, and demonstrate that the movable-sensor (MS) arrangement can substantially outperform a conventional fixed-position (FP) array, especially when the array becomes nonuniform. Simulation results corroborate the theory, showing CRB reductions with higher $P_0$, larger $M$, $N$, and $K$, and improved beampatterns via array interpolation and MUSIC processing, underscoring the practical value of group MS optimization for semi-passive IRS sensing.

Abstract

The performance of the sensing system is limited by the signal attenuation and the number of receiving components. In this letter, we investigate the sensor position selection in a semi-passive intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) sensing system. The IRS consists of passive elements and active sensors, where the sensors can receive and process the echo signal for direction-of-arrival (DoA) estimation. Motivated by the movable antenna array and fluid antenna system, we consider the case where the sensors are integrated into a group for movement and derive the corresponding Cramer-Rao bound (CRB). Then, the optimal solution for the positions of the movable sensors (MSs) to the CRB minimization problem is derived in closed form. Moreover, we characterize the relationship between the CRB and system parameters. Theoretical analysis and numerical results are provided to demonstrate the superiority of the proposed MS scheme over the fixed-position (FP) scheme.

Semi-Passive IRS Enabled Sensing with Group Movable Sensors

TL;DR

This work tackles accurate DoA estimation in a semi-passive IRS sensing system by optimizing the positions of group-movable sensors to minimize the Cramer-Rao bound (CRB). The authors derive the DoA CRB , show it scales inversely with , and obtain a closed-form optimal sensor-position solution under grouping constraints. They analyze how the number of groups and the available budget affect the CRB, and demonstrate that the movable-sensor (MS) arrangement can substantially outperform a conventional fixed-position (FP) array, especially when the array becomes nonuniform. Simulation results corroborate the theory, showing CRB reductions with higher , larger , , and , and improved beampatterns via array interpolation and MUSIC processing, underscoring the practical value of group MS optimization for semi-passive IRS sensing.

Abstract

The performance of the sensing system is limited by the signal attenuation and the number of receiving components. In this letter, we investigate the sensor position selection in a semi-passive intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) sensing system. The IRS consists of passive elements and active sensors, where the sensors can receive and process the echo signal for direction-of-arrival (DoA) estimation. Motivated by the movable antenna array and fluid antenna system, we consider the case where the sensors are integrated into a group for movement and derive the corresponding Cramer-Rao bound (CRB). Then, the optimal solution for the positions of the movable sensors (MSs) to the CRB minimization problem is derived in closed form. Moreover, we characterize the relationship between the CRB and system parameters. Theoretical analysis and numerical results are provided to demonstrate the superiority of the proposed MS scheme over the fixed-position (FP) scheme.

Paper Structure

This paper contains 5 sections, 3 theorems, 8 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Solution and Performance Analysis
  4. Simulation Results
  5. Conclusion

Key Result

Proposition 1

One optimal solution to problem prob_var is When $L$ is odd, another optimal solution to problem prob_var is

Figures (4)

  • Figure 1: Illustration of the semi-passive IRS enabled sensing system.
  • Figure 2: CRB versus transmit power $P_0$.
  • Figure 3: CRB versus number of sensors $K$.
  • Figure 4: Beampattern versus target's DoA $\theta$.

Theorems & Definitions (5)

  • Proposition 1
  • Remark 1
  • Proposition 2
  • Remark 2
  • Proposition 3