# 1-bit Massive MU-MIMO Precoding in VLSI

arXiv: https://arxiv.org/abs/1702.03449 · HTML: https://mlsanity.com/paper/1702.03449/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2017.

## Built on by (most cited first, 7 of 7)

- [Asymptotic SEP Analysis and Optimization of Linear-Quantized Precoding in Massive MIMO Systems](https://mlsanity.com/paper/2307.06714/) (2023)
- [Asymptotic Analysis of One-bit Quantized Box-Constrained Precoding in Large-Scale Multi-User Systems](https://mlsanity.com/paper/2502.02953/) (2025)
- [Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems](https://mlsanity.com/paper/2502.13390/) (2025)
- [Training Channel Selection for Learning-based 1-bit Precoding in Massive MU-MIMO](https://mlsanity.com/paper/2502.11653/) (2025)
- [Cauchy-Schwarz Regularizers](https://mlsanity.com/paper/2503.01639/) (2025)
- [Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach](https://mlsanity.com/paper/2507.10634/) (2025)
- [Multibit Quantized Precoding for MU-mMIMO](https://mlsanity.com/paper/2607.15959/) (2026)

## Abstract (arXiv)

Massive multiuser (MU) multiple-input multiple-output (MIMO) will be a core technology in fifth-generation (5G) wireless systems as it offers significant improvements in spectral efficiency compared to existing multi-antenna technologies. The presence of hundreds of antenna elements at the base station (BS), however, results in excessively high hardware costs and power consumption, and requires high interconnect throughput between the baseband-processing unit and the radio unit. Massive MU-MIMO that uses low-resolution analog-to-digital and digital-to-analog converters (DACs) has the potential to address all these issues. In this paper, we focus on downlink precoding for massive MU-MIMO systems with 1-bit DACs at the BS. The objective is to design precoders that simultaneously mitigate multi-user interference (MUI) and quantization artifacts. We propose two nonlinear 1-bit precoding algorithms and corresponding very-large scale integration (VLSI) designs. Our algorithms rely on biconvex relaxation, which enables the design of efficient 1-bit precoding algorithms that achieve superior error-rate performance compared to that of linear precoding algorithms followed by quantization. To showcase the efficacy of our algorithms, we design VLSI architectures that enable efficient 1-bit precoding for massive MU-MIMO systems in which hundreds of antennas serve tens of user equipments. We present corresponding field-programmable gate array (FPGA) implementations to demonstrate that 1-bit precoding enables reliable and high-rate downlink data transmission in practical systems.