Finite-alphabet MMSE equalization for all-digital massive MU-MIMO mmWave communications
Journal article, 2020

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 naïve 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 3.9× and 5.8×, respectively.

massive multi-user MIMO

spatial equalization


Millimeter wave (mmWave)

hardware implementation.

minimum mean-square error (MMSE)


Oscar Castaneda

Cornell University

Sven Jacobsson


Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Giuseppe Durisi

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Tom Goldstein

University of Maryland

Christoph Studer

Cornell University

IEEE Journal on Selected Areas in Communications

0733-8716 (ISSN)

Vol. 38 9 2128 -2141 9110827

Massive MIMO systems with low-resolution converters

Swedish Foundation for Strategic Research (SSF), 2015-03-01 -- 2020-02-28.

Massive MIMO systems and nonlinear hardware impairments

Swedish Foundation for Strategic Research (SSF), 2014-01-01 -- 2015-12-31.

Subject Categories


Communication Systems

Signal Processing



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