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

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.