# Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning

arXiv: https://arxiv.org/abs/2405.11816 · HTML: https://mlsanity.com/paper/2405.11816/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 16 of 16)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks](https://mlsanity.com/paper/1911.11523/) (2019)
- [Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning](https://mlsanity.com/paper/1708.06235/) (2017)
- [A Distributed Massive MIMO Channel Sounder for "Big CSI Data"-driven Machine Learning](https://mlsanity.com/paper/2206.15302/) (2022)
- [On Deep Learning-based Massive MIMO Indoor User Localization](https://mlsanity.com/paper/1804.04826/) (2018)
- [A Tutorial on Terahertz-Band Localization for 6G Communication Systems](https://mlsanity.com/paper/2110.08581/) (2021)
- [CSI-Based Localization with CNNs Exploiting Phase Information](https://mlsanity.com/paper/2101.08983/) (2021)
- [CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network](https://mlsanity.com/paper/2205.05775/) (2022)
- [Reliable Deep Learning based Localization with CSI Fingerprints and Multiple Base Stations](https://mlsanity.com/paper/2111.11839/) (2021)
- [Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioning](https://mlsanity.com/paper/2210.14510/) (2022)
- [Deep Learning based Positioning with Multi-task Learning and Uncertainty-based Fusion](https://mlsanity.com/paper/2403.08565/) (2024)
- [A Meta-learning based Generalizable Indoor Localization Model using Channel State Information](https://mlsanity.com/paper/2305.13453/) (2023)
- [ML-Assisted UE Positioning: Performance Analysis and 5G Architecture Enhancements](https://mlsanity.com/paper/2108.11365/) (2021)

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

- [RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models](https://mlsanity.com/paper/2606.01899/) (2026)

## Abstract (arXiv)

Utilizing deep learning (DL) techniques for radio-based positioning of user equipment (UE) through channel state information (CSI) fingerprints has demonstrated significant potential. DL models can extract complex characteristics from the CSI fingerprints of a particular environment and accurately predict the position of a UE. Nonetheless, the effectiveness of the DL model trained on CSI fingerprints is highly dependent on the particular training environment, limiting the trained model's applicability across different environments. This paper proposes a novel DL model structure consisting of two parts, where the first part aims at identifying features that are independent from any specific environment, while the second part combines those features in an environment specific way with the goal of positioning. To train such a two-part model, we propose the multi-environment meta-learning (MEML) approach for the first part to facilitate training across various environments, while the second part of the model is trained solely on data from a specific environment. Our findings indicate that employing the MEML approach for initializing the weights of the DL model for a new unseen environment significantly boosts the accuracy of UE positioning in the new target environment as well the reliability of its uncertainty estimation. This method outperforms traditional transfer learning methods, whether direct transfer learning (DTL) between environments or completely training from scratch with data from a new environment. The proposed approach is verified with real measurements for both line-of-sight (LOS) and non-LOS (NLOS) environments.