  We investigate mismatched data detection for massive multi-user (MU)
multiple-input multiple-output (MIMO) wireless systems in which the prior
distribution of the transmit signal used in the data detector differs from the
true prior. In order to minimize the performance loss caused by the prior
mismatch, we include a tuning stage into the recently proposed large-MIMO
approximate message passing (LAMA) algorithm, which enables the development of
data detectors with optimal as well as sub-optimal parameter tuning. We show
that carefully-selected priors enable the design of simpler and computationally
more efficient data detection algorithms compared to LAMA that uses the optimal
prior, while achieving near-optimal error-rate performance. In particular, we
demonstrate that a hardware-friendly approximation of the exact prior enables
the design of low-complexity data detectors that achieve near
individually-optimal performance. Furthermore, for Gaussian priors and uniform
priors within a hypercube covering the quadrature amplitude modulation (QAM)
constellation, our performance analysis recovers classical and recent results
on linear and non-linear massive MU-MIMO data detection, respectively.
