# Deep-Learning Based Linear Precoding for MIMO Channels with Finite-Alphabet Signaling

arXiv: https://arxiv.org/abs/2111.03504 · HTML: https://mlsanity.com/paper/2111.03504/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Powers of Tensors and Fast Matrix Multiplication](https://mlsanity.com/paper/1401.7714/) (2014)
- [Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness](https://mlsanity.com/paper/2006.16646/) (2020)
- [Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding](https://mlsanity.com/paper/2006.15495/) (2020)

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

- [Detecting Malicious Pilot Contamination in Multiuser Massive MIMO Using Decision Trees](https://mlsanity.com/paper/2510.03831/) (2025)

## Abstract (arXiv)

This paper studies the problem of linear precoding for multiple-input multiple-output (MIMO) communication channels employing finite-alphabet signaling. Existing solutions typically suffer from high computational complexity due to costly computations of the constellation-constrained mutual information. In contrast to existing works, this paper takes a different path of tackling the MIMO precoding problem. Namely, a data-driven approach, based on deep learning, is proposed. In the offline training phase, a deep neural network learns the optimal solution on a set of MIMO channel matrices. This allows the reduction of the computational complexity of the precoder optimization in the online inference phase. Numerical results demonstrate the efficiency of the proposed solution vis-`a-vis existing precoding algorithms in terms of significantly reduced complexity and close-to-optimal performance.