This paper presents a new integrated sensing and communication (ISAC) framework, leveraging the recent advancements of reconfigurable distributed antenna and reflecting surface (RDARS). RDARS is a programmable surface structure comprising numerous elements, each of which can be flexibly configured to operate either in a reflection mode, resembling a passive reconfigurable intelligent surface (RIS), or in a connected mode, functioning as a remote transmit or receive antenna. Our RDARS-aided ISAC framework effectively mitigates the adverse impact of multiplicative fading when compared to the passive RIS-aided ISAC, and reduces cost and energy consumption when compared to the active RIS-aided ISAC. Within our RDARS-aided ISAC framework, we consider a radar output signal-to-noise ratio (SNR) maximization problem under communication constraints to jointly optimize the active transmit beamforming matrix of the base station (BS), the reflection and mode selection matrices of RDARS, and the receive filter. To tackle the inherent non-convexity and the binary integer optimization introduced by the mode selection in this optimization challenge, we propose an efficient iterative algorithm with proved convergence based on majorization minimization (MM) and penalty-based methods.Numerical and simulation results demonstrate the superior performance of our new framework, and clearly verify substantial distribution, reflection as well as selection gains obtained by properly configuring the RDARS.

翻译：本文提出了一种新的通感一体化（ISAC）框架，利用了可重构分布式天线与反射表面（RDARS）的最新进展。RDARS是一种可编程表面结构，包含众多单元，每个单元均可灵活配置为反射模式（类似于无源可重构智能表面RIS）或连接模式（作为远程发射或接收天线）。与基于无源RIS的ISAC相比，我们的RDARS辅助ISAC框架有效缓解了乘性衰落的不利影响；与基于有源RIS的ISAC相比，则降低了成本和能耗。在该RDARS辅助ISAC框架中，我们考虑在通信约束下优化雷达输出信噪比（SNR）问题，以联合优化基站（BS）的有源发射波束赋形矩阵、RDARS的反射与模式选择矩阵以及接收滤波器。为应对该优化问题中固有的非凸性以及模式选择引入的二元整数优化，我们提出了一种基于主要化最小化（MM）和惩罚方法的高效迭代算法，并证明了其收敛性。数值与仿真结果表明，新框架具有优越性能，并通过合理配置RDARS清晰验证了其显著的分布、反射及选择增益。