# Asymptotic Analysis of One-bit Quantized Box-Constrained Precoding in Large-Scale Multi-User Systems

arXiv: https://arxiv.org/abs/2502.02953 · HTML: https://mlsanity.com/paper/2502.02953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 11 of 11)

- [What Will 5G Be?](https://mlsanity.com/paper/1405.2957/) (2014)
- [1-bit Massive MU-MIMO Precoding in VLSI](https://mlsanity.com/paper/1702.03449/) (2017)
- [Massive MIMO 1-Bit DAC Transmission: A Low-Complexity Symbol Scaling Approach](https://mlsanity.com/paper/1709.08278/) (2017)
- [A Framework for One-Bit and Constant-Envelope Precoding over Multiuser Massive MISO Channels](https://mlsanity.com/paper/1810.03159/) (2018)
- [Interference Exploitation 1-Bit Massive MIMO Precoding: A Partial Branch-and-Bound Solution with Near-Optimal Performance](https://mlsanity.com/paper/1908.03678/) (2019)
- [Efficient CI-Based One-Bit Precoding for Multiuser Downlink Massive MIMO Systems with PSK Modulation](https://mlsanity.com/paper/2110.11628/) (2021)
- [Sharp Analysis of RLS-based Digital Precoder with Limited PAPR in Massive MIMO](https://mlsanity.com/paper/2205.12077/) (2022)
- [Asymptotic SEP Analysis and Optimization of Linear-Quantized Precoding in Massive MIMO Systems](https://mlsanity.com/paper/2307.06714/) (2023)
- [PAPR-Limited Precoding in Massive MIMO Systems with Reflect- and Transmit-Array Antennas](https://mlsanity.com/paper/1912.00485/) (2019)
- [Discrete MMSE Precoding for Multiuser MIMO Systems with PSK Modulation](https://mlsanity.com/paper/2105.11555/) (2021)
- [Performance Analysis of Joint Antenna Selection and Precoding Methods in Multi-user Massive MISO](https://mlsanity.com/paper/2409.04924/) (2024)

## Built on by (most cited first, 1 of 1)

- [Asymptotic Analysis of Nonlinear One-Bit Precoding in Massive MIMO Systems via Approximate Message Passing](https://mlsanity.com/paper/2509.13955/) (2025)

## Abstract (arXiv)

This paper addresses the design of multi-antenna precoding strategies, considering hardware limitations such as low-resolution digital-to-analog converters (DACs), which necessitate the quantization of transmitted signals. The typical approach starts with optimizing a precoder, followed by a quantization step to meet hardware requirements. This study analyzes the performance of a quantization scheme applied to the box-constrained regularized zero-forcing (RZF) precoder in the asymptotic regime, where the number of antennas and users grows proportionally. The box constraint, initially designed to cope with low-dynamic range amplifiers, is used here to control quantization noise rather than for amplifier compatibility. A significant challenge in analyzing the quantized precoder is that the input to the quantization operation does not follow a Gaussian distribution, making traditional methods such as Bussgang's decomposition unsuitable. To overcome this, the paper extends the Gordon's inequality and introduces a novel Gaussian Min-Max Theorem to model the distribution of the channel-distorted precoded signal. The analysis derives the tight lower bound for the signal-to-distortion-plus-noise ratio (SDNR) and the bit error rate (BER), showing that optimal tuning of the amplitude constraint improves performance.