Quantification of Errors of the Performance Estimators in the Linear-Quantized Precoding Models for Massive MIMO Systems

TL;DR

This paper analyzes the errors in performance estimators for linear-quantized precoding in massive MIMO systems, providing error bounds and convergence insights to improve practical system design under hardware constraints.

Contribution

It derives error bounds for key performance metrics and demonstrates the convergence of finite-dimensional solutions to asymptotic limits in quantized precoding models.

Findings

Error bounds for SINR and SEP established

Finite-dimensional solutions converge to asymptotic values

Framework supports robust precoding design under hardware constraints

Abstract

Massive MIMO (Multiple-Input Multiple-Output) is a key enabler for 5G and future wireless systems, boosting channel capacity, energy efficiency, and spectral efficiency. However, high power consumption and hardware costs of Digital-to-Analog Converters (DACs) in massive MIMO create practical challenges. To mitigate these, recent work proposes low-resolution DACs-restricting transmitted signals to finite voltage levels-to cut power and costs. This requires studying quantized precoding: signals are processed via a linear precoding matrix, then quantized by DACs. In this paper, we explore the linear-quantized precoding model and its statistically or asymptotically equivalent variants. We derive error bounds for two key metrics:Signal-to-Interference-plus-Noise Ratio (SINR) and Symbol Error Probability (SEP), based on the linear-quantized model and its equivalent counterparts. We also formulate and analyze the SINR maximization problem in both asymptotic and finite-dimensional scenarios. Our analysis shows that as system dimensions scale, finite-dimensional problem solutions/values converge to their asymptotic equivalents-underscoring the practical value of asymptotic insights with stability guarantees. These findings theoretically support robust precoding design under hardware constraints, enabling efficient massive MIMO implementation with low-resolution DACs. Beyond validating asymptotic predictions in finite regimes, our framework offers practical optimization guidelines for real-world systems, linking theory and applications.