# CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach

arXiv: https://arxiv.org/abs/1806.07447 · HTML: https://mlsanity.com/paper/1806.07447/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting](https://mlsanity.com/paper/2310.03762/) (2023)
- [Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification](https://mlsanity.com/paper/2305.18206/) (2023)
- [Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning](https://mlsanity.com/paper/2410.19788/) (2024)
- [Benchmarking Learnt Radio Localisation under Distribution Shift](https://mlsanity.com/paper/2210.01930/) (2022)
- [Adaptive Attention-Based Model for 5G Radio-based Outdoor Localization](https://mlsanity.com/paper/2503.23810/) (2025)
- [IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing](https://mlsanity.com/paper/2609.06131/) (2026)

## Abstract (arXiv)

We report on experimental results on the use of a learning-based approach to infer the location of a mobile user of a cellular network within a cell, for a 5G-type Massive multiple input, multiple output (MIMO) system. We describe how the sample spatial covariance matrix computed from the CSI can be used as the input to a learning algorithm which attempts to relate it to user location. We discuss several learning approaches, and analyze in depth the application of extreme learning machines, for which theoretical approximate performance benchmarks are available, to the localization problem. We validate the proposed approach using experimental data collected on a Huawei 5G testbed, provide some performance and robustness benchmarks, and discuss practical issues related to the deployment of such a technique in 5G networks.