A Grant-Free Access and Data Recovery Method for Massive Machine-Type Communications

The surge of demand of the Internet of Things (IoTs) poses more requirements for cellular communication systems, such as higher levels of reliability, latency and supported number of devices. A common scenario of IoT is massive machinetype communications (mMTC). The characteristics of mMTC, i.e. the massive number of sensors, small packets and sporadic transmission, require novel methods to detect the active devices, estimate their channel state information and recovery their data with high accuracy and low latency. In this paper, we propose a new compressive sensing based method to jointly conduct active device detection, channel estimation and data recovery. As a high level description of the proposed method, we improve the performance of active device detection and channel estimation by using side information brought from the data recovery, and improve the performance of data recovery in successive interference cancellation by using the side information brought from the active device detection. Simulation results demonstrate that the proposed method has improved data recovery performance.