Message Passing Based Joint Channel and User Activity Estimation for Uplink Grant-Free Massive MIMO Systems With Low-Precision ADCs

This letter considers the problem of a joint estimation for channel fading and user activity in an uplink grant-free massive MIMO system equipped with low-precision analog-to-digital converters (ADCs). Different from existing works, the joint estimation is formalized as a non-overlapping group problem, where the components of compound channel involving user activity indicator and channel fading are independent with condition distribution rather than independent Bernoulli-Gaussian. Based on this new formulation, a new algorithm leveraging hybrid generalized approximate passing (HyGAMP) is then developed including GAMP part (channel estimation) and loopy belief propagation (LBP) part (user activity detection), where the strong correlation among elements in each row of the channel matrix can be decoupled in LBP part. By exchanging the information between the GAMP part and the LBP part, the proposed algorithm improves the performance of channel estimation and user activity detection as compared to earlier results. In addition, the simulation results verify that the proposed algorithm develops the performance of conventional methods dramatically.