To exploit the potential of the reconfigurable intelligent surface (RIS) in supporting the future integrated sensing and communication (ISAC), this paper proposes a novel passive beamforming strategy for the RIS-enabled ISAC (RIS-ISAC) system in consideration of the target size. To this end, the detection probability for target sensing is derived in closed-form based on the illumination power on an approximated scattering surface area (SSA) of the target, and a new concept of ultimate detection resolution (UDR) is defined for the first time to measure the capability of the target detection. Subsequently, an optimization problem is formulated to maximize the signal-to-noise ratio (SNR) at the user-equipment (UE) under a minimum detection probability constraint. To solve this problem, a novel convexification process is performed to convexify the detection probability constraint with matrix operations and a real-valued first-order Taylor approximation. The semidefinite relaxation (SDR) is then adopted to relax the problem. A successive convex approximation (SCA) based algorithm is finally designed to yield a phase-shift solution, followed by a detailed analysis on the problem feasibility condition as well as the algorithm convergence. Our results reveal the inherent trade-offs between the sensing and the communication performances, and between the UDR and the duration of a sensing time slot. In comparison with two existing approaches, the proposed strategy is validated to be superior when detecting targets with practical sizes.
翻译:为了利用可再整合的智能表面(RIS)在支持未来综合遥感和通信(ISAC)方面的潜力,本文件建议,考虑到目标大小,为ISAC(RIS-ISAC)系统制定一项新的被动波束化战略;为此,目标感测的概率以封闭形式产生,其依据是目标大致分散的表面(SSA)的照明力,并首次确定了最终探测解析(UDR)的新概念,以衡量目标探测能力。随后,为用户设备(ISAC)系统制定了一个新的被动波束化战略,以在最低检测概率限制下最大限度地实现用户设备(ISAC)的信号对噪音比。为了解决这个问题,根据对目标大致分散的表面区域(SSA)的光亮度能力,对目标的探测概率限制进行了新的拼凑。随后,为缓解问题采用了半定型解析(SDR)的新概念。随后,基于USAC(SAC)的算法最终设计了在用户设备(SNRIS)的信号对信号对音比率比率(SNRR)的最大值,在最低限度的概率限制下,在用户-National-Nationalationalationalizationalizalizalationalationalationality 和Lislational)之间,然后进行一项详细的分析,在我们对内测算测算法的进度和两次测算法的测算法的测算。