  We consider a multi-user multiple-input multiple-output (MU-MIMO) system that
uses orthogonal frequency division multiplexing (OFDM). Several receivers are
developed for data detection of MU-MIMO transmissions where two users share the
same OFDM time and frequency resources. The receivers have partial state
information about the MU-MIMO transmission with each receiver having knowledge
of the MU-MIMO channel, however the modulation constellation of the
co-scheduled user is unknown. We propose a joint maximum likelihood (ML)
modulation classification of the co-scheduled user and data detection receiver
using the max-log-MAP approximation. It is shown that the decision metric for
the modulation classification is an accumulation over a set of tones of
Euclidean distance computations that are also used by the max-log-MAP detector
for bit log-likelihood ratio (LLR) soft decision generation. An efficient
hardware implementation emerges that exploits this commonality between the
classification and detection steps and results in sharing of the hardware
resources. Comparisons of the link performance of the proposed receiver to
several linear receivers is demonstrated through computer simulations. It is
shown that the proposed receiver offers \unit[1.5]{dB} improvement in
signal-to-noise ratio (SNR) over the nulling projection receiver at $1\%$ block
error rate (BLER) for $64$-QAM with turbo code rate of $1/2$ in the case of
zero transmit and receiver antenna correlations. However, in the case of high
antenna correlation, the linear receiver approaches suffer significant loss
relative to the optimal receiver.
