A Compressive Sensing-Based Active User and Symbol Detection Technique for Massive Machine-Type Communications

Byeong Kook Jeong, Byonghyo Shim, Kwang Bok Lee

Abstract: In massive machine-type communication (mMTC) systems, a large number of machine-type devices sporadically transmit small packets with low rates. By exploiting the sporadic activity of machine-type devices, we can cast the detection problem as the compressive sensing-based multi-user detection (CS-MUD). In this paper, we propose a novel CS-MUD algorithm for the active user and symbol detection based on a maximum a posteriori probability (MAP) criterion. By exchanging extrinsic information between active user detector and symbol detector, the proposed algorithm improves the performance of active user detection and the reliability of symbol estimate. Numerical simulations demonstrate that the proposed algorithm achieves outstanding MUD performance.