Iterative Reweighed Approach for Multiuser Detection with Multiple Measurement Vector in MTC Communications

Abstract

The most promising feature of the fifth generation (5G) wireless networks is to support the Internet of Things (IoT) application, such as massive machine-type communications (mMTC). In mMTC scenario, a large number of users are connected to an access point, but very few of them are active at the same time, which motivates us to exploit Low-Activity Code Division Multiple Access (LA-CDMA) as the multiple access technology for MTC. The optimal maximum a posterior probability (MAP) assumes that the user activity factor is exactly known, which is in fact unknown in the practical detection scenarios. In this paper, we first formulate the LA-CDMA uplink into a multiple measurement vector (MMV) model for several continuous time slots, then we introduce a novel iterative reweighed (IR) algorithm to reconstruct the sparse signal. The new scheme overcomes the difficulty of unknown user activity factor and the simulation results over massive MTC systems demonstrate that the proposed algorithm achieves substantial performance gain over traditional detectors considerably.