Massive MIMO with 1-Bit DACs: Data Detection for Quantized Linear Precoding with Dithering

TL;DR

This paper introduces a maximum-likelihood data detection method for massive MIMO systems with 1-bit DACs, significantly improving symbol error rates by leveraging dithering and linear MMSE estimation.

Contribution

It proposes a novel ML detection approach for 1-bit quantized massive MIMO with dithering, enhancing detection accuracy over existing methods.

Findings

Achieves over two orders of magnitude reduction in symbol error rate.

Demonstrates effectiveness of dithering combined with ML detection.

Provides a practical solution for power-efficient massive MIMO systems.

Abstract

To leverage high-frequency bands in 6G wireless systems and beyond, employing massive multiple-input multipleoutput (MIMO) arrays at the transmitter and/or receiver side is crucial. To mitigate the power consumption and hardware complexity across massive frequency bands and antenna arrays, a sacrifice in the resolution of the data converters will be inevitable. In this paper, we consider a point-to-point massive MIMO system with 1-bit digital-to-analog converters at the transmitter, where the linearly precoded signal is supplemented with dithering before the 1-bit quantization. For this system, we propose a new maximumlikelihood (ML) data detection method at the receiver by deriving the mean and covariance matrix of the received signal, where symbol-dependent linear minimum mean squared error estimation is utilized to efficiently linearize the transmitted signal. Numerical results show that the proposed ML method can provide gains of more than two orders of magnitude in terms of symbol error rate over conventional data detection based on soft estimation.