Channel Estimation and Device Detection for Reconfigurable Intelligent Surface Aided Massive IoT Networks With Mixed-ADCs

The Reconfigurable intelligent surface (RIS) is a prospective technique for future massive Internet of things communication networks due to its ability of favorable propagation environment customization and controllability. Besides, the equipment of large scale antenna arrays with mixed analog-to-digital converters (ADCs) is expected to make a good compromise between performance improvements and hardware costs. In this letter we estimate the channel state information and detect the device activity with the assist of the RIS in the scenario of massive connectivity. By utilizing the Bayes theory, the nonlinear performance analysis is proposed in the RIS-aided massive IoT network with mixed quantization operation. Numerical results demonstrate the efficient channel estimation and active device detection with relatively low complexity. Moreover, theoretical analysis results are obtained by deriving the nonlinear multiple measurement vector equations in the regime of RIS-assisted massive connectivity with mixed-ADCs.