Joint Active User Detection and Channel Estimation Via Bayesian Learning Approaches in MTC Communications

To support the massive machine-type communications (mMTC) scenario for Internet of Things (IoTs) applications featured by large-scale device connectivity and low activity, grant-free non-orthogonal multiple access (GF-NOMA) compressive sensing (CS)-based multi-user detection methods (MUD) are developed. In this paper, we develop two Bayesian CS-based methods, i.e., sparse Learning (SBL) fast inverse-free (FI-SBL), joint MUD channel estimation (CE) in GF-NOMA with Low-Activity Code Division Multiple Access (LA-CDMA) as technology. SBL is investigated robust CE utilizing parameterized Gaussian prior information. Then to resolve high computational complexity SBL, FI-SBL proposed, which replaces matrix reversion operations relaxed evidence lower bound. Simulation results show that proposed algorithms outperform traditional reduces significantly.