In this paper, we investigate a novel reconfigurable distributed antennas and reflecting surface (RDARS) aided multi-user massive MIMO system with imperfect CSI and propose a practical two-timescale (TTS) transceiver design to reduce the communication overhead and computational complexity of the system. In the RDARS-aided system, not only distribution gain but also reflection gain can be obtained by a flexible combination of the distributed antennas and reflecting surface, which differentiates the system from the others and also makes the TTS design challenging. To enable the optimal TTS transceiver design, the achievable rate of the system is first derived in closed-form. Then the TTS design aiming at the weighted sum rate maximization is considered. To solve the challenging non-convex optimization problem with high-order design variables, i.e., the transmit powers and the phase shifts at the RDARS, a block coordinate descent based method is proposed to find the optimal solutions in semi-closed forms iteratively. Specifically, two efficient algorithms are proposed with provable convergence for the optimal phase shift design, i.e., Riemannian Gradient Ascent based algorithm by exploiting the unit-modulus constraints, and Two-Tier Majorization-Minimization based algorithm with closed-form optimal solutions in each iteration. Simulation results validate the effectiveness of the proposed algorithm and demonstrate the superiority of deploying RDARS in massive MIMO systems to provide substantial rate improvement with a significantly reduced total number of active antennas/RF chains and lower transmit power when compared to the DAS and RIS-aided systems.

翻译：本文研究了一种新型可重构分布式天线与反射面（RDARS）辅助的多用户 Massive MIMO 系统，在非完美信道状态信息（CSI）条件下，提出了一种实用的双时间尺度（TTS）收发器设计方案，以降低系统的通信开销和计算复杂度。在 RDARS 辅助系统中，通过分布式天线与反射面的灵活组合，不仅能获得分布增益，还能获得反射增益，这使得该系统区别于其他系统，同时也增加了 TTS 设计的难度。为实现最优的 TTS 收发器设计，首先推导了系统可达速率的闭式表达式。随后，考虑以加权和速率最大化为目标的 TTS 设计。针对包含高阶设计变量（即发射功率和 RDARS 处的相移）的复杂非凸优化问题，提出了一种基于块坐标下降的方法，迭代求解半闭式形式的最优解。具体而言，针对最优相移设计，提出了两种具有可证明收敛性的高效算法：一是基于黎曼梯度上升算法，利用单位模约束；二是基于两层最大化-最小化（Majorization-Minimization）算法，每步迭代中具有闭式最优解。仿真结果验证了所提算法的有效性，并展示了在 Massive MIMO 系统中部署 RDARS 的优越性：与 DAS 和 RIS 辅助系统相比，RDARS 能以显著减少的有源天线/射频链路总数和更低的发射功率，实现大幅度的速率提升。