  We investigate the performance of mismatched data detection in large
multiple-input multiple-output (MIMO) systems, where the prior distribution of
the transmit signal used in the data detector differs from the true prior. To
minimize the performance loss caused by this prior mismatch, we include a
tuning stage into our recently-proposed large MIMO approximate message passing
(LAMA) algorithm, which allows us to develop mismatched LAMA algorithms with
optimal as well as sub-optimal tuning. We show that carefully-selected priors
often enable simpler and computationally more efficient algorithms compared to
LAMA with the true prior while achieving near-optimal performance. A
performance analysis of our algorithms for a Gaussian prior and a uniform prior
within a hypercube covering the QAM constellation recovers classical and recent
results on linear and non-linear MIMO data detection, respectively.
