# Data Augmentation Empowered Neural Precoding for Multiuser MIMO with MMSE Model

arXiv: https://arxiv.org/abs/2203.02196 · HTML: https://mlsanity.com/paper/2203.02196/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2022.

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

- [Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics](https://mlsanity.com/paper/2208.02724/) (2022)
- [A Model-based GNN for Learning Precoding](https://mlsanity.com/paper/2212.00860/) (2022)
- [MemFine: Memory-Aware Fine-Grained Scheduling for MoE Training](https://mlsanity.com/paper/2511.21431/) (2025)
- [Learning Model-Free Robust Precoding for Cooperative Multibeam Satellite Communications](https://mlsanity.com/paper/2303.11427/) (2023)

## Abstract (arXiv)

Precoding design exploiting deep learning methods has been widely studied for multiuser multiple-input multiple-output (MU-MIMO) systems. However, conventional neural precoding design applies black-box-based neural networks which are less interpretable. In this paper, we propose a deep learning-based precoding method based on an interpretable design of a neural precoding network, namely iPNet. In particular, the iPNet mimics the classic minimum mean-squared error (MMSE) precoding and approximates the matrix inversion in the design of the neural network architecture. Specifically, the proposed iPNet consists of a model-driven component network, responsible for augmenting the input channel state information (CSI), and a data-driven sub-network, responsible for precoding calculation from this augmented CSI. The latter data-driven module is explicitly interpreted as an unsupervised learner of the MMSE precoder. Simulation results show that by exploiting the augmented CSI, the proposed iPNet achieves noticeable performance gain over existing black-box designs and also exhibits enhanced generalizability against CSI mismatches.