  Reconfigurable intelligent surfaces (RISs) have been introduced as a remedy
for mitigating frequent blockages in millimeter wave (mmWave) multiple-input
multiple-output (MIMO) communication networks. However, perfect or nearly
perfect channel state information (CSI) is fundamental in order to achieve
their full potential. Traditionally, an RIS is fully passive without any
baseband processing capabilities, which poses great challenges for CSI
acquisition. Thus, we focus on the hybrid RIS architecture, where a small
portion of RIS elements are active and able to processing the received pilot
signals for estimating the corresponding channel. The channel estimation (CE)
is done by resorting to off-the-grid compressive sensing technique, i.e.,
atomic norm minimization, for exacting channel parameters through two stages.
Simulation results show that the proposed scheme outperforms the passive RIS CE
under the same training overhead.
