# Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO

arXiv: https://arxiv.org/abs/1903.03718 · HTML: https://mlsanity.com/paper/1903.03718/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Adversarial Neural Networks for Error Correcting Codes](https://mlsanity.com/paper/2112.11491/) (2021)
- [Variational Autoencoders for Precoding Matrices with High Spectral Efficiency](https://mlsanity.com/paper/2111.15626/) (2021)
- [Training Channel Selection for Learning-based 1-bit Precoding in Massive MU-MIMO](https://mlsanity.com/paper/2502.11653/) (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)

## Abstract (arXiv)

Base station (BS) architectures for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems are equipped with hundreds of antennas to serve tens of users on the same time-frequency channel. The immense number of BS antennas incurs high system costs, power, and interconnect bandwidth. To circumvent these obstacles, sophisticated MU precoding algorithms that enable the use of 1-bit DACs have been proposed. Many of these precoders feature parameters that are, traditionally, tuned manually to optimize their performance. We propose to use deep-learning tools to automatically tune such 1-bit precoders. Specifically, we optimize the biConvex 1-bit PrecOding (C2PO) algorithm using neural networks. Compared to the original C2PO algorithm, our neural-network optimized (NNO-)C2PO achieves the same error-rate performance at bf 2boldsymbol× lower complexity. Moreover, by training NNO-C2PO for different channel models, we show that 1-bit precoding can be made robust to vastly changing propagation conditions.