# Meta-Transfer Learning for mmWave Beam Alignment

arXiv: https://arxiv.org/abs/2607.00860 · HTML: https://mlsanity.com/paper/2607.00860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [Learning Site-Specific Probing Beams for Fast mmWave Beam Alignment](https://mlsanity.com/paper/2107.13121/) (2021)
- [Embedding Model Based Fast Meta Learning for Downlink Beamforming Adaptation](https://mlsanity.com/paper/2109.09086/) (2021)
- [Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks](https://mlsanity.com/paper/2501.17883/) (2025)

## Abstract (arXiv)

Millimeter-wave (mmWave) beam alignment is critical for next-generation wireless systems, but existing approaches either update all parameters during adaptation or restrict updates to a subset of layers without adapting intermediate feature representations. We propose MTL-BA, a meta-transfer learning framework for multiple-input single-output (MISO) beam alignment that freezes a pre-trained convolutional backbone and meta-learns lightweight Scale-and-Shift (SS) adapters and a classifier head. Under an outdoor-to-indoor shift in DeepMIMO, MTL-BA achieves the highest Top-1 accuracy among the evaluated methods at 35 dB while updating 17× fewer parameters than full fine-tuning and meta-training 6× faster than MAML.