Reconfigurable Intelligent Surface for Massive Connectivity
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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.
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