Individual Channel Estimation in RIS-Aided MIMO Systems Using Atomic Norm Minimization

Channel estimation is fundamental to leveraging the performance gains of reconfigurable intelligent surfaces (RISs), one of the key technologies for 6G. Due to the passive nature of RIS, most current research focuses on cascaded channel estimation. However, individual channel information is crucial for practical applications such as flexible precoding design. In this paper, we propose a hybrid RIS architecture integrated with dynamically controllable active elements, which reduces the cost of RIS deployment. Based on this novel architecture, we introduce an atomic norm minimization (ANM)-based individual channel estimation method, exploiting the sparse characteristics of high frequency channels. We theoretically prove that our proposed method retains its applicability even in the presence of random element damage in RIS. Furthermore, we extend the solution for individual channel estimation to passive RIS scenario under the mild condition that the locations of base station and RIS are known. Simulation experiments demonstrate that the proposed methods achieve super-resolution channel estimation, surpassing the performance of existing methods such as orthogonal matching pursuit.