# SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming

arXiv: https://arxiv.org/abs/2401.10513 · HTML: https://mlsanity.com/paper/2401.10513/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2024.

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

- [A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites](https://mlsanity.com/paper/2502.08757/) (2025)
- [Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding](https://mlsanity.com/paper/2502.08758/) (2025)
- [A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff](https://mlsanity.com/paper/2507.18587/) (2025)
- [A Low-Complexity Plug-and-Play Deep Learning Model for Generalizable Massive MIMO Precoding](https://mlsanity.com/paper/2601.21897/) (2026)

## Abstract (arXiv)

Deep learning (DL)-based solutions have emerged as promising candidates for beamforming in massive Multiple-Input Multiple-Output (mMIMO) systems. Nevertheless, it remains challenging to seamlessly adapt these solutions to practical deployment scenarios, typically necessitating extensive data for fine-tuning while grappling with domain adaptation and generalization issues. In response, we propose a novel approach combining Meta-Learning Domain Generalization (MLDG) with novel data augmentation techniques during fine-tuning. This approach not only accelerates adaptation to new channel environments but also significantly reduces the data requirements for fine-tuning, thereby enhancing the practicality and efficiency of DL-based mMIMO systems. The proposed approach is validated by simulating the performance of a backbone model when deployed in a new channel environment, and with different antenna configurations, path loss, and base station height parameters. Our proposed approach demonstrates superior zero-shot performance compared to existing methods and also achieves near-optimal performance with significantly fewer fine-tuning data samples.