Joint Active and Passive Beamforming Design for Reconfigurable Intelligent Surface Enabled Integrated Sensing and Communication

Xing, Zhe; Wang, Rui; Yuan, Xiaojun

doi:10.1109/tcomm.2023.3244246

Cited by 15 publications

(7 citation statements)

References 49 publications

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“…In the following, we prove that ( 18) and ( 19) are feasible solutions for Problem (17). It can be seen that constraints (17c)-(17e) and (17g) hold obviously with the solutions, then we only need to verify constraints (17b) and (17f).…”

Section: B Optimize Wmentioning

confidence: 83%

“…Therefore, it is verified that the constructed solution ŵc,k and ŵr are feasible solution for Problem (17). Besides, to achieve a better converged solution, we propose a transformation of Problem (17) by replacing the QoS constraint (17b) with a tighter constraint. Specifically, we use the constraint…”

Section: Constraint (17b) Holds Withmentioning

confidence: 88%

“…In this scenario, two primary performance indicators for sensing were considered. The contributions of [14], [17]- [19] optimized the beampattern at the target, which ensured that more power was illuminated to the target for sensing. In contrast, the authors of [20] first considered a four-hop BS-RIS-target-RIS-BS link and investigated the received Radar signal-to-interference-plus noise-ratio (SINR) at the DFRC BS.…”

Section: Index Termsmentioning

confidence: 99%

“…By dropping the rank-one constraints (17d), the relaxed problem is convex and can be solved via CVX. Denoting R and Rk as a feasible solution of Problem (17), we can construct a…”

Section: B Optimize Wmentioning

confidence: 99%

“…, where ξ 2 is larger than ξ 1 .The tighter QoS constraint encourages the BS to beam its power towards the communication UEs initially, which allows more flexibility in optimizing the reflecting coefficient matrix Φ and maximizing the Radar SINR. Therefore, this transformation is expected to lead to a better solution for Problem (17).…”

Section: Constraint (17b) Holds Withmentioning

confidence: 99%

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Active RIS Aided Integrated Sensing and Communication Systems

Yu¹,

Gui²,

Hong³

et al. 2023

Preprint

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This paper considers an active reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. We aim to maximize Radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the beamforming matrix at the dual-function Radar-communication (DFRC) base station (BS) and the reflecting coefficient matrix at the active RIS subject to the quality of service (QoS) constraint of communication users (UE) and the transmit power constraints of active RIS and DFRC BS. In the proposed scenario, we mainly focus on the four-hop BS-RIS-target-RIS-BS sensing link, and the direct BS-target-BS link is assumed to be blocked. Due to the coupling of the beamforming matrix and the reflecting coefficient matrix, we use the alternating optimization (AO) method to solve the problem. Given reflecting coefficients, we apply majorization-minimization (MM) and semidefinite programming (SDP) methods to deal with the nonconvex QoS constraints and Radar SINR objective functions. An initialization method is proposed to obtain a high-quality converged solution, and a sufficient condition of the feasibility of the original problem is provided. Since the signal for sensing is reflected twice at the same active RIS panel, the Radar SINR and active RIS transmit power are quartic functions of RIS coefficients after using the MM algorithm. We then transform the problem into a sum of square (SOS) form, and a semidefinite relaxation (SDR)-based algorithm is developed to solve the problem. Finally, simulation results validate the potential of active RIS in enhancing the performance of the ISAC system compared to the passive RIS, and indicate that the transmit power and physical location of the active RIS should be carefully chosen.

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Section: B Optimize Wmentioning

confidence: 83%

Section: Constraint (17b) Holds Withmentioning

confidence: 88%

Section: Index Termsmentioning

confidence: 99%

“…By dropping the rank-one constraints (17d), the relaxed problem is convex and can be solved via CVX. Denoting R and Rk as a feasible solution of Problem (17), we can construct a…”

Section: B Optimize Wmentioning

confidence: 99%

Section: Constraint (17b) Holds Withmentioning

confidence: 99%

See 3 more Smart Citations