  A fundamental challenge in millimeter-wave (mmWave) communication is the
susceptibility to blocking objects. One way to alleviate this problem is the
use of reconfigurable intelligent surfaces (RIS). Nevertheless, due to the
large number of passive reflecting elements on RIS, channel estimation turns
out to be a challenging task. In this paper, we address the channel estimation
for RIS-aided mmWave communication systems based on a localization method. The
proposed idea consists of exploiting the sparsity of the mmWave channel and the
topology of the RIS. In particular, we first propose the concept of reflecting
unit set (RUS) to improve the flexibility of RIS. We then propose a novel
coplanar maximum likelihood-based (CML) 3D positioning method based on the RUS,
and derive the Cramer-Rao lower bound (CRLB) for the positioning method.
Furthermore, we develop an efficient positioning-based channel estimation
scheme with low computational complexity. Compared to state-of-the-art methods,
our proposed method requires less time-frequency resources in channel
acquisition as the complexity is independent to the total size of the RIS but
depends on the size of the RUSs, which is only a small portion of the RIS.
Large performance gains are confirmed in simulations, which proves the
effectiveness of the proposed method.
