# A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites

arXiv: https://arxiv.org/abs/2502.08757 · HTML: https://mlsanity.com/paper/2502.08757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 12 Feb 2025.

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

- [Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation](https://mlsanity.com/paper/2009.10812/) (2020)
- [Teacher-Student Architecture for Knowledge Distillation: A Survey](https://mlsanity.com/paper/2308.04268/) (2023)
- [Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming](https://mlsanity.com/paper/2007.00038/) (2020)
- [A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming](https://mlsanity.com/paper/2206.10499/) (2022)
- [SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming](https://mlsanity.com/paper/2401.10513/) (2024)

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

- [A Low-Complexity Plug-and-Play Deep Learning Model for Generalizable Massive MIMO Precoding](https://mlsanity.com/paper/2601.21897/) (2026)

## Abstract (arXiv)

Massive multiple-input multiple-output (mMIMO) technology has transformed wireless communication by enhancing spectral efficiency and network capacity. This paper proposes a novel deep learning-based mMIMO precoder to tackle the complexity challenges of existing approaches, such as weighted minimum mean square error (WMMSE), while leveraging meta-learning domain generalization and a teacher-student architecture to improve generalization across diverse communication environments. When deployed to a previously unseen site, the proposed model achieves excellent sum-rate performance while maintaining low computational complexity by avoiding matrix inversions and by using a simpler neural network structure. The model is trained and tested on a custom ray-tracing dataset composed of several base station locations. The experimental results indicate that our method effectively balances computational efficiency with high sum-rate performance while showcasing strong generalization performance in unseen environments. Furthermore, with fine-tuning, the proposed model outperforms WMMSE across all tested sites and SNR conditions while reducing complexity by at least 73×.