Joint Beamforming Design for RIS-Assisted Integrated Sensing and Communication Systems

Luo, Honghao; Liu, Rang; Li, Ming; Liu, Yang; Liu, Qian

doi:10.1109/tvt.2022.3197448

Cited by 78 publications

(19 citation statements)

References 13 publications

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“…To facilitate the algorithm development, we attempt to re-arrange the new objective function (23) into explicit and compact forms with respect to w and φ, respectively. By stacking the vectors w j , ∀j, into w and applying h T k (φ)w j = h T d,k w j + h T r,k diag{Gw j }φ, the following equivalent expressions for F (w, φ, r, c) can be obtained:…”

Section: A Fp-based Transformationmentioning

confidence: 99%

“…Studies for different RIS deployment scenarios with different communication and radar sensing metrics have been carried out [21]- [28]. The initial works [21]- [23] assumed that the RIS only assists with communications since it is deployed near the users with negligible impact on the target echoes. To further exploit the benefits of RIS, especially in improving radar sensing performance, the authors in [24]- [26] assumed that the RIS is deployed where the direct links between the base station (BS) and the users/target are blocked.…”

Section: Introductionmentioning

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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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: A Fp-based Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

“…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. To support scenarios with multiple point-like targets, the authors of [18] developed a majorization-minimization algorithm for target tracking by collaboratively designing the transmit beampattern and RIS coefficients. In [19], the authors conceived a joint optimization scheme regarding the sensing waveform and the RIS coefficients from the perspective of sensing mutual information.…”

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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“…In [ 28 ], the author jointly optimized the RIS reflection coefficients and the number of tunable reflective elements to improve the quality of service (QoS) of RIS-assisted edge networks by considering the actual amplitude and phase shift model of the RIS. The authors in [ 29 ] proposed an efficient alternating algorithm for fractional programming (FP), majorization–minimization (MM), and manifold optimization methods to jointly optimize active and passive beamforming under multiple constraints of radar sensing similarity, RIS constraints and transmit power constraints. Moreover, the authors in [ 30 ] considered the performance of RIS-assisted integrated satellite unmanned aerial vehicle (UAV)-terrestrial networks, and the closed-form expression for the outage probability (OP) was obtained to evaluate the impact of the introduction of RIS on the system performance.…”

Section: Introductionmentioning

confidence: 99%

Joint Beamforming Design for RIS-Assisted Integrated Satellite-HAP-Terrestrial Networks Using Deep Reinforcement Learning

Zhu

et al. 2023

Sensors

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In this paper, we consider reconfigurable intelligent surface (RIS)-assisted integrated satellite high-altitude platform terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting the HAP stability and RIS reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipment (UE) to the satellite. To aim at maximizing the system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle effectively by traditional solving methods. Based on this, this paper studies the deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance, execution time, and computing speed, making real-time decision making truly feasible.

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Joint Beamforming Design for RIS-Assisted Integrated Sensing and Communication Systems

Cited by 78 publications

References 13 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?

Joint Beamforming Design for RIS-Assisted Integrated Satellite-HAP-Terrestrial Networks Using Deep Reinforcement Learning

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