# Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding

arXiv: https://arxiv.org/abs/2006.15495 · HTML: https://mlsanity.com/paper/2006.15495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

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

- [Deep-Learning Based Linear Precoding for MIMO Channels with Finite-Alphabet Signaling](https://mlsanity.com/paper/2111.03504/) (2021)

## Abstract (arXiv)

Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumption in massive MU-MIMO, low-resolution digital-to-analog converters (DAC) for each antenna and radio frequency (RF) chain in downlink transmission is used, which brings challenges for precoding design. To circumvent these obstacles, we develop a model-driven deep learning (DL) network for massive MU-MIMO with finite-alphabet precoding in this article. The architecture of the network is specially designed by unfolding an iterative algorithm. Compared with the traditional state-of-the-art techniques, the proposed DL-based precoder shows significant advantages in performance, complexity, and robustness to channel estimation error under Rayleigh fading channel.