论文标题

可重新配置智能表面的联合主动和被动横梁形成设计,启用了集成感应和通信

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

论文作者

Xing, Zhe, Wang, Rui, Yuan, Xiaojun

论文摘要

为了利用RIS在支持ISAC方面的潜力,本文提出了针对目标尺寸的新型关节主动和被动光束设计,用于支持RIS的ISAC系统。首先,基于目标的近似散射表面积上的照明功率,以封闭形式得出了目标传感的检测概率,首次定义了最终检测分辨率(UDR)的新概念,以测量目标检测能力。然后,在最小检测概率约束下制定了优化问题,以最大化UE处的SNR。为了解决非凸问题,开发了一种新颖的替代优化方法。在这种方法中,通过我们提出的基于双式搜索的方法获得了通信和传感光束器的解决方案。最佳接收组合向量是从等效的雷利高品质问题得出的。为了优化RIS相移,进行了Charnes-Cooper转换以应对分数目标,并提出了新的共介化过程,以将检测概率约束与矩阵操作和实现的一阶taylor taylor膨胀相结合。凸化后,基于连续的凸近似值(SCA)算法旨在产生次优迁移溶液。最后,构建了整体优化算法,然后对其计算复杂性,收敛行为和问题可行性条件进行详细分析。进行了广泛的模拟,以证明拟议的横梁形成设计的分析特性,并揭示了两个重要的权衡,即交流与传感权衡以及UDR与UDR与Sensing-Dration折衷权衡。与几个现有基准相比,我们提出的方法在检测具有实际尺寸的目标时被验证为优越。

To exploit the potential of the RIS in supporting ISAC, this paper proposes a novel joint active and passive beamforming design for RIS-enabled ISAC system in consideration of the target size. First, the detection probability for target sensing is derived in closed-form based on the illumination power on an approximated scattering surface area of the target, and a new concept of ultimate detection resolution (UDR) is defined for the first time to measure the target detection capability. Then, an optimization problem is formulated to maximize the SNR at the UE under a minimum detection probability constraint. To solve the non-convex problem, a novel alternative optimization approach is developed. In this approach, the solutions of the communication and sensing beamformers are obtained by our proposed bisection-search based method. The optimal receive combining vector is derived from an equivalent Rayleigh-quotient problem. To optimize the RIS phase shifts, the Charnes-Cooper transformation is conducted to cope with the fractional objective, and a novel convexification process is proposed to convexify the detection probability constraint with matrix operations and a real-valued first-order Taylor expansion. After the convexification, a successive convex approximation (SCA) based algorithm is designed to yield a suboptimal phase-shift solution. Finally, the overall optimization algorithm is built, followed by detailed analysis on its computational complexity, convergence behavior and problem feasibility condition. Extensive simulations are carried out to testify the analytical properties of the proposed beamforming design, and to reveal two important trade-offs, namely, communication vs. sensing trade-off and UDR vs. sensing-duration trade-off. In comparison with several existing benchmarks, our proposed approach is validated to be superior when detecting targets with practical sizes.

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