  This paper addresses the problem of joint downlink channel estimation and
user grouping in massive multiple-input multiple-output (MIMO) systems, where
the motivation comes from the fact that the channel estimation performance can
be improved if we exploit additional common sparsity among nearby users. In the
literature, a commonly used group sparsity model assumes that users in each
group share a uniform sparsity pattern. In practice, however, this
oversimplified assumption usually fails to hold, even for physically close
users. Outliers deviated from the uniform sparsity pattern in each group may
significantly degrade the effectiveness of common sparsity, and hence bring
limited (or negative) gain for channel estimation. To better capture the group
sparse structure in practice, we provide a general model having two sparsity
components: commonly shared sparsity and individual sparsity, where the
additional individual sparsity accounts for any outliers. Then, we propose a
novel sparse Bayesian learning (SBL)-based framework to address the joint
channel estimation and user grouping problem under the general sparsity model.
The framework can fully exploit the common sparsity among nearby users and
exclude the harmful effect from outliers simultaneously. Simulation results
reveal substantial performance gains over the existing state-of-the-art
baselines.
