Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication

TL;DR

This paper introduces finite-alphabet MMSE equalization for massive MU-MIMO mmWave systems, achieving high performance with low-resolution hardware through novel algorithms and VLSI implementation benefits.

Contribution

It proposes FAME, a new finite-alphabet MMSE equalization method with algorithms for near-optimal performance using 1-3 bit quantization.

Findings

FAME significantly outperforms naive quantization of MMSE.

Quantized coefficients with 1-3 bits achieve near-optimal performance.

VLSI results show at least 3.9x reduction in power and 5.8x in area.

Abstract

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.