Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound optimization

Song, Xianxin; Xu, Jie; Liu, Fan; Han, Tony Xiao; Eldar, Yonina C.

doi:10.1109/gcwkshps56602.2022.10008725

Cited by 40 publications

(52 citation statements)

References 15 publications

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“…in which the expressions for the sub-matrices of F IM can be derived according to (17) and (18) as shown in detail in Appendix A. We should emphasize here that each element of F IM is a function of both W and φ.…”

Section: Crb For Parameter Estimation In Sensingmentioning

confidence: 99%

“…According to (17), the derivatives of η with respect to each parameter can be calculated as where Ḣt (φ) and Ḧt (φ) denote the partial derivatives of H t (φ) with respect to θ 1 and θ 2 , respectively. Thus, plugging (66) into (18), the elements of F IM can be calculated as…”

Section: Appendix Amentioning

confidence: 99%

“…researchers have begun exploring the deployment of RIS in radar sensing [18] and ISAC systems [19], [20]. Studies for different RIS deployment scenarios with different communication and radar sensing metrics have been carried out [21]- [28].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systems

Liu¹,

Li²,

Liu³

et al. 2023

Preprint

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In this paper, we investigate the integration of integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RIS) for providing wide-coverage and ultra-reliable communication and high-accuracy sensing functions. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multi-user multi-input single-output (MU-MISO) communications and radar sensing with the assistance of an RIS. We focus on both target detection and parameter estimation performance in terms of the signal-to-noise ratio (SNR) and Cramér-Rao bound (CRB), respectively. Two optimization problems are formulated for maximizing the achievable sum-rate of the multi-user communications under an SNR constraint for target detection or a CRB constraint for parameter estimation, the transmit power budget, and the unit-modulus constraint of the RIS reflection coefficients. Efficient algorithms are developed to solve these two complicated non-convex problems. Extensive simulation results demonstrate the advantages of the proposed joint beamforming and reflection designs compared with other schemes. In addition, it is shown that more RIS reflection elements bring larger performance gains for direct-of-arrival (DoA) estimation than for target detection.

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Section: Crb For Parameter Estimation In Sensingmentioning

confidence: 99%

Section: Appendix Amentioning

confidence: 99%

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SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systems

Liu¹,

Li²,

Liu³

et al. 2023

Preprint

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show abstract

“…More recently, it is claimed that the proper exploitation of RISs in ISAC systems can further boost the sensing performance [17]- [21]. In [17], the authors proposed a RIS-assisted ISAC framework, in which a RIS is employed to establish reliable line-of-sight (LoS) links for distance and velocity estimation.…”

Section: A Prior Workmentioning

confidence: 99%

“…Considering the practical restriction of discrete phase shifts, a constantmodulus sensing waveform was designed in [20], in which the multi-user interference was minimized under the sensing CRB constraint. In [21], the authors considered utilizing the RIS to provide sensing services to the blind-zone target, where the CRB minimization problems were investigated in the cases of point target and extended target, respectively. However, the direct combination of RIS and ISAC in the aforementioned works inevitably increased the number of reflections experienced by the echo signals, which restricted the sensing performance.…”

Section: A Prior Workmentioning

confidence: 99%

STARS-ISAC: How Many Sensors Do We Need?

Zhang¹,

Liu²,

Wang³

et al. 2023

Preprint

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A simultaneously transmitting and reflecting surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where a novel bi-directional sensing-STARS architecture is devised to facilitate the full-space communication and sensing. Based on the proposed framework, a joint optimization problem is formulated, where the Cramér-Rao bound (CRB) for estimating the 2dimension direction-of-arrival of the sensing target is minimized. Two cases are considered for sensing performance enhancement. 1) For the two-user case, an alternating optimization algorithm is proposed.In particular, the maximum number of deployable sensors is obtained in the closed-form expressions.2) For the multi-user case, an extended CRB (ECRB) metric is proposed to characterize the impact of the number of sensors on the sensing performance. Based on the proposed metric, a novel penaltybased double-loop (PDL) algorithm is proposed to solve the ECRB minimization problem. To tackle the coupling of the ECRB, a general decoupling approach is proposed to convert it to a tractable weighted linear summation form. Simulation results reveal that 1) the proposed PDL algorithm can achieve a nearoptimal performance with consideration of sensor deployment; 2) without violating the communication under the quality of service requirements, reducing the receive antennas at the BS does not deteriorate the sensing performance; and 3) it is preferable to deploy more passive elements than sensors in terms of achieving optimal sensing performance.

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

Zhu

Lyu

Xiang

et al. 2023

Sci. China Inf. Sci.

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Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound optimization

Cited by 40 publications

References 15 publications

SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systems

SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systems

STARS-ISAC: How Many Sensors Do We Need?

Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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