# High-Precision Machine-Learning Based Indoor Localization with Massive MIMO System

arXiv: https://arxiv.org/abs/2303.03743 · HTML: https://mlsanity.com/paper/2303.03743/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 7 Mar 2023.

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

- [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)
- [DNN-based Localization from Channel Estimates: Feature Design and Experimental Results](https://mlsanity.com/paper/2004.00363/) (2020)
- [CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion](https://mlsanity.com/paper/2009.02798/) (2020)
- [Intelligent Reflecting Surface Enhanced Indoor Robot Path Planning: A Radio Map based Approach](https://mlsanity.com/paper/2009.12804/) (2020)
- [MaMIMO CSI-based positioning using CNNs: Peeking inside the black box](https://mlsanity.com/paper/2003.04581/) (2020)

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

- [LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization](https://mlsanity.com/paper/2309.02961/) (2023)

## Abstract (arXiv)

High-precision cellular-based localization is one of the key technologies for next-generation communication systems. In this paper, we investigate the potential of applying machine learning (ML) to a massive multiple-input multiple-output (MIMO) system to enhance localization accuracy. We analyze a new ML-based localization pipeline that has two parallel fully connected neural networks (FCNN). The first FCNN takes the instantaneous spatial covariance matrix to capture angular information, while the second FCNN takes the channel impulse responses to capture delay information. We fuse the estimated coordinates of these two FCNNs for further accuracy improvement. To test the localization algorithm, we performed an indoor measurement campaign with a massive MIMO testbed at 3.7GHz. In the measured scenario, the proposed pipeline can achieve centimeter-level accuracy by combining delay and angular information.