  Reconfigurable intelligent surfaces (RISs) are expected to be a key component
enabling the mobile network evolution towards a flexible and intelligent 6G
wireless platform. In most of the research works so far, RIS has been treated
as a passive base station (BS) with a known state, in terms of its location and
orientation, to boost the communication and/or terminal positioning
performance. However, such performance gains cannot be guaranteed anymore when
the RIS state is not perfectly known. In this paper, by taking the RIS state
uncertainty into account, we formulate and study the performance of a joint RIS
calibration and user positioning (JrCUP) scheme. From the Fisher information
perspective, we formulate the JrCUP problem in a network-centric single-input
multiple-output (SIMO) scenario with a single BS, and derive the analytical
lower bound for the states of both user and RIS. We also demonstrate the
geometric impact of different user locations on the JrCUP performance while
also characterizing the performance under different RIS sizes. Finally, the
study is extended to a multi-user scenario, shown to further improve the state
estimation performance.
