# Multibit Quantized Precoding for MU-mMIMO

arXiv: https://arxiv.org/abs/2607.15959 · HTML: https://mlsanity.com/paper/2607.15959/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Hyperparameter optimization with approximate gradient](https://mlsanity.com/paper/1602.02355/) (2016)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [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)
- [On Optimal Regularization Parameters via Bilevel Learning](https://mlsanity.com/paper/2305.18394/) (2023)
- [From Theory to Reality: A Design Framework for Integrated Communication and Computing Receivers](https://mlsanity.com/paper/2411.02016/) (2024)
- [6D Rigid Body Localization and Velocity Estimation via Gaussian Belief Propagation](https://mlsanity.com/paper/2412.12133/) (2024)
- [Fairness-Aware Secure Integrated Sensing and Communications with Fractional Programming](https://mlsanity.com/paper/2507.11224/) (2025)
- [Fractional Programming and Manifold Optimization for Reciprocal BD-RIS Scattering Matrix Design](https://mlsanity.com/paper/2511.07683/) (2025)
- [Regularized Approximate Message Passing for Overloaded Discrete Linear Inversion](https://mlsanity.com/paper/2604.03717/) (2026)

## Abstract (arXiv)

We propose a novel multibit quantized precoding method for the downlink of multi-user massive MIMO systems with low-resolution digital-to-analog converters. The new method, termed multibit quantized precoding (MQP), enforces the finite-alphabet constraint through an l0-norm penalty, approximated by a smooth surrogate so as to yield a reformulated problem, which is then convexized via fractional programming, ultimately extending quantized precoding beyond 1-bit alphabets. The regularization parameter of the proposed method is selected via a discrepancy principle integrated with graduated non-convexity continuation, resulting in a principled and reproducible hyperparameter tuning method and an efficient iterative algorithm with a closed-form, least-squares-type update per iteration. In order to further reduce the computational complexity of the method, we include a Gaussian belief propagation (GaBP) step for turning the least-squares update in linear-time. Simulations performed for systems with different sizes demonstrate that both methods, namely the MQP with and without GaBP, achieve competitive or superior error-rate performance compared to state-of-the-art quantized precoding algorithms under various channel conditions.