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