Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks.

翻译：本文研究了在可重构智能表面（RIS）辅助下的无蜂窝大规模多输入多输出（MIMO）系统。针对存在空间相关性的场景，设计了RIS相移以改善信道估计性能。具体而言，我们采用线性最小均方误差（LMMSE）估计方法，推导了聚合信道的信道估计表达式及估计误差表达式。随后，构建了一个在实用相移约束下最小化平均归一化均方误差（NMSE）的优化问题。为规避问题固有的非凸性，我们提出了一种增强型差分进化算法，该算法通过对部分高性能差分进化个体引入增强算子，能够有效避免陷入局部最优。数值结果表明，相较于现有先进基准方案，我们提出的算法能显著提升信道估计质量。