# Optimal Multiuser Loading in Quantized Massive MIMO under Spatially Correlated Channels

## TL;DR

This paper analyzes the performance of low-resolution DAC massive MIMO systems with correlated channels, optimizing the regularization parameter for RZF precoding and deriving the optimal user loading ratio.

## Contribution

It provides a closed-form solution for the optimal regularization parameter and user loading ratio considering quantization errors and channel correlation in massive MIMO systems.

## Key findings

- Optimal regularization parameter increases linearly with user loading ratio
- Optimal user loading ratio is independent of ADC resolution and channel correlation
- Higher DAC resolution increases the optimal user loading ratio

## Abstract

Low-resolution digital-to-analog converter (DAC) has shown great potential in facilitating cost- and power-efficient implementation of massive multiple-input multiple-output (MIMO) systems. We investigate the performance of a massive MIMO downlink network with low-resolution DACs using regularized zero-forcing (RZF) precoding. It serves multiple receivers equipped with finite-resolution analog-to-digital converters (ADCs). By taking the quantization errors at both the transmitter and receivers into account under spatially correlated channels, the regularization parameter for RZF is optimized with a closed-form solution by applying the asymptotic random matrix theory. The optimal regularization parameter increases linearly with respect to the user loading ratio while independent of the ADC quantization resolution and the channel correlation. Furthermore, asymptotic sum rate performance is characterized and a closed-form expression for the optimal user loading ratio is obtained at low signal-to-noise ratio. The optimal ratio increases with the DAC resolution while it decreases with the ADC resolution. Numerical simulations verify our observations.