# Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioning

arXiv: https://arxiv.org/abs/1810.04126 · HTML: https://mlsanity.com/paper/1810.04126/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 16 of 16)

- [Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network](https://mlsanity.com/paper/2101.12090/) (2021)
- [Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review](https://mlsanity.com/paper/2205.14935/) (2022)
- [CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network](https://mlsanity.com/paper/2205.05775/) (2022)
- [Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network](https://mlsanity.com/paper/2102.03130/) (2021)
- [Federated Distillation based Indoor Localization for IoT Networks](https://mlsanity.com/paper/2205.11440/) (2022)
- [MaxRay: A Raytracing-based Integrated Sensing and Communication Framework](https://mlsanity.com/paper/2112.01751/) (2021)
- [Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization](https://mlsanity.com/paper/2504.08811/) (2025)
- [LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking](https://mlsanity.com/paper/2412.05074/) (2024)
- [Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training](https://mlsanity.com/paper/2206.06592/) (2022)
- [Benchmarking Learnt Radio Localisation under Distribution Shift](https://mlsanity.com/paper/2210.01930/) (2022)
- [Resource-aware Deep Learning for Wireless Fingerprinting Localization](https://mlsanity.com/paper/2211.01759/) (2022)
- [Neural 5G Indoor Localization with IMU Supervision](https://mlsanity.com/paper/2402.09948/) (2024)
- [Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform](https://mlsanity.com/paper/2504.17173/) (2025)
- [Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data](https://mlsanity.com/paper/2102.04892/) (2021)
- [Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization](https://mlsanity.com/paper/2408.06452/) (2024)
- [A Configuration-First Framework for Reproducible, Low-Code Machine Learning: a Localization Use Case](https://mlsanity.com/paper/2510.25692/) (2025)

## Abstract (arXiv)

With a significant increase in area throughput, Massive MIMO has become an enabling technology for fifth generation (5G) wireless mobile communication systems. Although prototypes were built, an openly available dataset for channel impulse responses to verify assumptions, e.g. regarding channel sparsity, is not yet available. In this paper, we introduce a novel channel sounder architecture, capable of measuring multiantenna and multi-subcarrier channel state information (CSI) at different frequency bands, antenna geometries and propagation environments. The channel sounder has been verified by evaluation of channel data from first measurements. Such datasets can be used to study various deep-learning (DL) techniques in different applications, e.g., for indoor user positioning in three dimensions, as is done in this paper. Not only we do achieve an accuracy better than 75 cm for line of sight (LoS), as is comparable to state-of-the-art conventional positioning techniques, but also obtain similar precision for the more challenging case of non-line of sight (NLoS). Further extensive indoor/outdoor measurement campaigns will provide a more comprehensive open CSI dataset, tagged with positions, for the scientific community to further test various algorithms.