Soft-Output Finite Alphabet Equalization for mmWAVE Massive MIMO

TL;DR

This paper introduces an improved finite-alphabet equalization method with soft-output capabilities for mmWave massive MU-MIMO systems, achieving high performance with low-bit quantization to reduce power and complexity.

Contribution

It develops an unbiased estimation and soft-output computation approach for finite-alphabet equalization in coded mmWave MU-MIMO systems, enhancing performance with minimal quantization bits.

Findings

Soft-output finite-alphabet equalization performs well with 1-3 bits per matrix entry.

The method achieves competitive error rates in challenging mmWave channels.

Simulation results validate the efficiency of the proposed approach.

Abstract

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.