  Reconfigurable intelligent surfaces (RISs) are envisioned to play a key role
in future wireless communication networks. However, channel estimation in
RIS-aided wireless networks is challenging due to their passive nature and the
large number of reflective elements, leading to high channel estimation
overhead. Additionally, conventional methods like beam sweeping, which do not
rely on explicit channel state information, often struggle in managing
interference in multi-user networks. In this paper, we propose a novel approach
that leverages digital twins (DTs) of the physical environments to approximate
channels using electromagnetic 3D models and ray tracing, thus relaxing the
need for channel estimation and extensive over-the-air computations in
RIS-aided wireless networks. To address the digital twins channel approximation
errors, we further refine this approach with a DT-specific robust transmission
design that reliably meets minimum desired rates. The results show that our
method secures these rates over 90% of the time, significantly outperforming
beam sweeping, which achieves these rates less than 8% of the time due to its
poor management of transmitting power and interference.
