  We propose equalization-based data detection algorithms for all-digital
millimeter-wave (mmWave) massive multiuser multiple-input multiple-out
(MU-MIMO) systems that exploit sparsity in the beamspace domain to reduce
complexity. We provide a condition on the number of users, basestation
antennas, and channel sparsity for which beamspace equalization can be less
complex than conventional antenna-domain processing. We evaluate the
performance-complexity trade-offs of existing and new beamspace equalization
algorithms using simulations with realistic mmWave channel models. Our results
reveal that one of our proposed beamspace equalization algorithms achieves up
to 8x complexity reduction under line-of-sight conditions, assuming a
sufficiently large number of transmissions within the channel coherence
interval.
