Spike and Slab Prior Based Joint Sparse Channel Estimation and Multiuser Detection in MTC Communications

Machine-type communications (MTC) has become one of the key requirements to support Internet-of-Things (IoT) for wireless communications. Due to the fact of large-scale connectivity and low activity factor, we exploit Low-Activity Code Division Multiple Access (LA-CDMA) as the multiple access technology. In MTC communications, effective multiuser detection (MUD) algorithms are required. In previous work, a variety of MUD approaches have been designed with known channel state information (CSI). In this paper, we address the joint channel estimation (CE) and MUD problems for LA-CDMA uplink systems with unknown CSI in MTC communications. We introduce a novel spike and slab prior based Gibbs sampling (SS-GS) approach to reconstruct the signal. It is shown that the introduced spike and slab prior is more effective in promoting sparsity and sparse signal reconstruction, and the proposed SSGS scheme outperforms the conventional schemes for CE and MUD in MTC communications.