Reconfigurable Intelligent Surface for Massive Connectivity: Joint Activity Detection and Channel Estimation

Abstract

This paper considers the massive connectivity with the aid of a reconfigurable intelligent surface (RIS), where an enormous number of devices transmit sporadically to a base station (BS). The RIS establishes favorable signal propagation environments to enhance data transmission in massive connectivity in such a scenario. Nevertheless, the BS needs to detect active devices and estimate channels to support data transmission in RIS-assisted massive access systems. Furthermore, due to a large number of devices, it is impossible to assign orthogonal signature sequences to the devices as in most of the existing works on RIS-related channel estimation, which yields unique challenges. This paper shall consider a RIS-assisted uplink IoT network and solve the RIS-related activity detection and channel estimation problem. The BS detects the active devices and estimates the separated channels of the RIS-to-device link and the RIS-to-BS link by using non-orthogonal pilot sequences. 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 the Bayesian inference framework and reduce the computational complexity by approximating the algorithm with the central limit theorem and Taylor series expansion. Finally, extensive numerical experiments are conducted to verify the effectiveness of the proposed algorithm.