Grouping-Based Joint Active User Detection and Channel Estimation with Massive MIMO

This paper considers an uplink massive machine-type communication scenario with a massive number of antennas, where a large number of user devices are connected to a base station (BS) and the user traffic is sporadic. We propose a novel hybrid message passing (HMP) algorithm to achieve joint active detection and channel estimation (JADCE) by exploiting the channel characteristics in both the angular domain and user domain, which is expected to enhance the performance of JADCE compared with the conventional approaches without the knowledge of such a consideration. The user grouping is performed simultaneously with JADCE, which provides the prerequisites of the joint spatial division and multiplexing to achieve significant savings both in the downlink training and feedback of channel state information at the transmitter. Based on the acquired knowledge of user grouping, we further propose a per-group processing based JADCE (PGP-JADCE) approach. It significantly reduces the computational overhead for JADCE and the BS is capable to process all the user groups in parallel. Further, the analysis of detection error probabilities and channel estimation error of PGP-JADCE is provided.