  Optimal data detection in massive multiple-input multiple-output (MIMO)
systems requires prohibitive computational complexity. A variety of detection
algorithms have been proposed in the literature, offering different trade-offs
between complexity and detection performance. In this paper, we build upon
variational Bayes (VB) inference to design low-complexity multiuser detection
algorithms for massive MIMO systems. We first examine the massive MIMO
detection problem with perfect channel state information at the receiver (CSIR)
and show that a conventional VB method with known noise variance yields poor
detection performance. To address this limitation, we devise two new VB
algorithms that use the noise variance and covariance matrix postulated by the
algorithms themselves. We further develop the VB framework for massive MIMO
detection with imperfect CSIR. Simulation results show that the proposed VB
methods achieve significantly lower detection errors compared with existing
schemes for a wide range of channel models.
