  Next-generation wireless systems are expected to combine millimeter-wave
(mmWave) and massive multi-user multiple-input multiple-output (MU-MIMO)
technologies to deliver high data-rates. These technologies require the
basestations (BSs) to process high-dimensional data at extreme rates, which
results in high power dissipation and system costs. Finite-alphabet
equalization has been proposed recently to reduce the power consumption and
silicon area of uplink spatial equalization circuitry at the BS by coarsely
quantizing the equalization matrix. In this work, we improve upon
finite-alphabet equalization by performing unbiased estimation and soft-output
computation for coded systems. By simulating a massive MU-MIMO system that uses
orthogonal frequency-division multiplexing and per-user convolutional coding,
we show that soft-output finite-alphabet equalization delivers competitive
error-rate performance using only 1 to 3 bits per entry of the equalization
matrix, even for challenging mmWave channels.
