Channel estimation (CE) plays a key role in reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) communication systems, while it poses a challenging task due to the passive nature of RIS and the cascaded channel structures. In this paper, a partially decoupled atomic norm minimization (PDANM) framework is proposed for CE of RIS-aided MIMO systems, which exploits the three-dimensional angular sparsity of the channel. In particular, PDANM partially decouples the differential angles at the RIS from other angles at the base station and user equipment, reducing the computational complexity compared with existing methods. A reweighted PDANM (RPDANM) algorithm is proposed to further improve CE accuracy, which iteratively refines CE through a specifically designed reweighing strategy. Building upon RPDANM, we propose an iterative approach named RPDANM with adaptive phase control (RPDANM-APC), which adaptively adjusts the RIS phases based on previously estimated channel parameters to facilitate CE, achieving superior CE accuracy while reducing training overhead. Numerical simulations demonstrate the superiority of our proposed approaches in terms of running time, CE accuracy, and training overhead. In particular, the RPDANM-APC approach can achieve higher CE accuracy than existing methods within less than 40 percent training overhead while reducing the running time by tens of times.

翻译：信道估计（CE）在可重构智能表面（RIS）辅助的多输入多输出（MIMO）通信系统中起着关键作用，但由于RIS的无源特性和级联信道结构，这一任务极具挑战性。本文提出了一种部分解耦原子范数最小化（PDANM）框架，用于RIS辅助MIMO系统的信道估计，该框架利用信道的三维角度稀疏性。特别地，PDANM将RIS处的差分角度与基站和用户设备处的其他角度进行部分解耦，相较于现有方法降低了计算复杂度。进一步，本文提出了一种重加权PDANM（RPDANM）算法，通过特定设计的重加权策略迭代优化信道估计，从而提升估计精度。基于RPDANM，我们提出了一种名为自适应相位控制RPDANM（RPDANM-APC）的迭代方法，该方法根据先前估计的信道参数自适应调整RIS相位以辅助信道估计，在降低训练开销的同时实现了卓越的信道估计精度。数值仿真结果表明，所提方法在运行时间、信道估计精度和训练开销方面均具有优越性。特别地，RPDANM-APC方法在不足40%训练开销的情况下，可实现高于现有方法的信道估计精度，同时运行时间降低数十倍。