  With the rapid development of Internet of Things (IoT), massive machine-type
communication has become a promising application scenario, where a large number
of devices transmit sporadically to a base station (BS). Reconfigurable
intelligent surface (RIS) has been recently proposed as an innovative new
technology to achieve energy efficiency and coverage enhancement by
establishing favorable signal propagation environments, thereby improving data
transmission in massive connectivity. Nevertheless, the BS needs to detect
active devices and estimate channels to support data transmission in
RIS-assisted massive access systems, which yields unique challenges. This paper
shall consider an RIS-assisted uplink IoT network and aims to solve the
RIS-related activity detection and channel estimation problem, where the BS
detects the active devices and estimates the separated channels of the
RIS-to-device link and the RIS-to-BS link. Due to limited scattering between
the RIS and the BS, we model the RIS-to-BS channel as a sparse channel. As a
result, by simultaneously exploiting both the sparsity of sporadic transmission
in massive connectivity and the RIS-to-BS channels, we formulate the
RIS-related activity detection and channel estimation problem as a sparse
matrix factorization problem. Furthermore, we develop an approximate message
passing (AMP) based algorithm to solve the problem based on Bayesian inference
framework and reduce the computational complexity by approximating the
algorithm with the central limit theorem and Taylor series arguments. Finally,
extensive numerical experiments are conducted to verify the effectiveness and
improvements of the proposed algorithm.
