Soft-Output Finite Alphabet Equalization for mmWave Massive MIMO
Paper i proceeding, 2020

Nxt-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.


Oscar Castañeda

Cornell Tech

Sven Jacobsson

Ericsson AB

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Giuseppe Durisi

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Tom Goldstein

University of Maryland

Christoph Studer

Cornell Tech

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

Vol. 2020-May 1764-1767 9053097

2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Barcelona, Spain,







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