  We propose finite-alphabet equalization, a new paradigm that restricts the
entries of the spatial equalization matrix to low-resolution numbers, enabling
high-throughput, low-power, and low-cost hardware equalizers. To minimize the
performance loss of this paradigm, we introduce FAME, short for finite-alphabet
minimum mean-square error (MMSE) equalization, which is able to significantly
outperform a naive quantization of the linear MMSE matrix. We develop efficient
algorithms to approximately solve the NP-hard FAME problem and showcase that
near-optimal performance can be achieved with equalization coefficients
quantized to only 1-3 bits for massive multi-user multiple-input
multiple-output (MU-MIMO) millimeter-wave (mmWave) systems. We provide
very-large scale integration (VLSI) results that demonstrate a reduction in
equalization power and area by at least a factor of 3.9x and 5.8x,
respectively.
