# Adaptive Learning Strategies for AoA-Based Outdoor Localization: A Comprehensive Framework

arXiv: https://arxiv.org/abs/2605.05055 · HTML: https://mlsanity.com/paper/2605.05055/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 6 May 2026.

## Builds on (most cited first, 23 of 23)

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- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
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- [Meta-learning approaches for few-shot learning: A survey of recent advances](https://mlsanity.com/paper/2303.07502/) (2023)
- [MetaLoc: Learning to Learn Wireless Localization](https://mlsanity.com/paper/2211.04258/) (2022)
- [AMF: Aggregated Mondrian Forests for Online Learning](https://mlsanity.com/paper/1906.10529/) (2019)
- [A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals](https://mlsanity.com/paper/2012.11171/) (2020)
- [Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks](https://mlsanity.com/paper/2502.12622/) (2025)
- [Continual Learning-Based MIMO Channel Estimation: A Benchmarking Study](https://mlsanity.com/paper/2211.10753/) (2022)
- [Physical Layer Security -- from Theory to Practice](https://mlsanity.com/paper/2210.13261/) (2022)
- [Continual Learning for Wireless Channel Prediction](https://mlsanity.com/paper/2506.22471/) (2025)
- [AoA-Based Physical Layer Authentication in Analog Arrays under Impersonation Attacks](https://mlsanity.com/paper/2407.08282/) (2024)
- [ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition](https://mlsanity.com/paper/2504.20193/) (2025)
- [On the Robustness of AoA as an Authentication Feature Under Spoofing: Fundamental Limits from Misspecified Cramer Rao Theory](https://mlsanity.com/paper/2603.21219/) (2026)

## Abstract (arXiv)

Localization in 5G and 6G networks is essential for important use cases such as intelligent transportation, smart factories, and smart cities. Although deep learning has enabled improving localization accuracy, depending on the deployment scenario and the effort required for dataset collection campaigns on a given infrastructure, the training process for localization models can vary significantly. Furthermore, with respect to feature selection, recent works have demonstrated the robustness of angle-of-arrival (AoA) based localization. In view of these two points, we propose an adaptive framework for AoA-based localization that consists of two alternative learning strategies, each suited either for large or small training datasets. The proposed framework is evaluated on a real, massive multiple input multiple output (mMIMO) orthogonal frequency division multiplexing (OFDM) outdoor channel state information (CSI) dataset. First, we investigate offline learning when large training datasets are available; we propose a hierarchical framework that first distinguishes between line of sight (LoS) and non line of sight (NLoS) regions and then moves to more fine grained localization in the respective region. This approach provides high-performance localization through accumulated batch retraining and an integrated hyperparameter optimization mechanism. Second, when only a small training dataset is available, an online learning framework is proposed, using incremental tree-based and ensemble-based models for handling streaming data and continuously updating mode, as well as an online few-shot learning model for rapidly initializing new classes from a limited labeled support set. These results showcase that highly accurate robust localization can be achieved incrementally during network operation by exploiting online learning, alleviating the need for large dataset collection campaigns.