Reconfigurable Intelligent Surface Enhanced Massive IoT Systems With Nonlinear Measurements

Abstract

Reconfigurable intelligent surface (RIS) is a promising technology for future communication systems, and the channel propagation environment can be controlled in an RIS-assisted network. In this letter, the problem of the joint channel estimation and device activity detection is investigated in an RIS empowered massive Internet of Things system. To achieve a satisfactory trade off between the system performance and the hardware overhead, the mixed analog-to-digital converters (ADCs) are configured in the network. Due to the large number of antennas equipped at the base station, the channel estimation and activity detection can be performed from the perspective of multiple measurement vector. Furthermore, the nonlinear theoretical analysis of the channel estimation and the activity detection is provided based on the Bayesian theory. Numerical results verify the effectiveness of the proposed nonlinear channel estimation and active device detection in terms of error probability and computational complexity.