Cramér-Rao界优化用于RDARS辅助ISAC系统中的联合波束成形与模式选择
Cramér-Rao Bound Optimization for Joint Beamforming and Mode Selection in RDARS-Assisted ISAC Systems
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中文总结 AI 辅助
本文提出RDARS辅助ISAC框架，通过联合优化波束成形与动态模式选择最小化CRB，在保证通信SINR的同时实现优于RIS和DAS的感知-通信权衡。
中文摘要 AI 辅助
集成感知与通信（ISAC）是6G网络的基础，要求架构同时提升感知精度和通信可靠性。本文提出了一种可重构分布式天线与反射面（RDARS）辅助的ISAC框架，其中RDARS克服了传统无源可重构智能表面（RIS）和分布式天线系统（DAS）的局限性。通过使每个单元动态地工作在反射或连接模式，RDARS协同利用了反射增益、分布增益以及额外的模式选择增益。我们研究了基站发射波束成形与RDARS动态模式选择的联合优化，以最小化感知性能指标，即目标定位的Cramér-Rao界（CRB），同时保证多个通信用户所需的最低信干噪比（SINR）。为解决由此产生的非凸混合整数问题，我们开发了一种基于交替优化（AO）框架的高效迭代算法，有效利用了Majorization-Minimization（MM）和惩罚方法。综合仿真验证了所提出的设计，表明动态RDARS配置在位置误差界（PEB）和通信SINR之间实现了优越的权衡，显著优于基准的无源RIS和DAS系统。
英文摘要
Integrated Sensing and Communication (ISAC) is a foundation of 6G networks, demanding architectures that simultaneously enhance sensing accuracy and communication reliability. This paper presents a Reconfigurable Distributed Antenna and Reflecting Surface (RDARS) aided ISAC framework, where an RDARS overcomes the limitations of conventional passive Reconfigurable Intelligent Surfaces (RIS) and Distributed Antenna Systems (DAS). By enabling each element to dynamically operate in either \textit{reflection} or \textit{connection} mode, RDARS synergistically harnesses reflection gain, distribution gain, and an additional mode-selection gain. We investigate the joint optimization of transmit beamforming at the base station and dynamic mode selection at the RDARS to minimize the sensing performance metric, namely the Cramér-Rao Bound (CRB) for target localization, while guaranteeing a minimum required Signal-to-Interference-plus-Noise Ratio (SINR) for multiple communication users. To solve the resulting non-convex and mixed-integer problem, we develop an efficient iterative algorithm based on the Alternating Optimization (AO) framework, effectively leveraging Majorization-Minimization (MM) and Penalty methods. Comprehensive simulations validate the proposed design, demonstrating that the dynamic RDARS configuration achieves a superior trade-off between the Position Error Bound (PEB) and communication SINR, significantly outperforming benchmark passive RIS and DAS systems.
