# Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network

arXiv: https://arxiv.org/abs/2102.03130 · HTML: https://mlsanity.com/paper/2102.03130/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioning](https://mlsanity.com/paper/1810.04126/) (2018)
- [On Deep Learning-based Massive MIMO Indoor User Localization](https://mlsanity.com/paper/1804.04826/) (2018)
- [Towards Practical Indoor Positioning Based on Massive MIMO Systems](https://mlsanity.com/paper/1905.11858/) (2019)
- [Fingerprinting-Based Positioning in Distributed Massive MIMO Systems](https://mlsanity.com/paper/1509.00202/) (2015)

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

- [CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network](https://mlsanity.com/paper/2205.05775/) (2022)
- [Federated Distillation based Indoor Localization for IoT Networks](https://mlsanity.com/paper/2205.11440/) (2022)
- [Towards Sustainable Deep Learning for Wireless Fingerprinting Localization](https://mlsanity.com/paper/2201.09071/) (2022)
- [Supervised Contrastive CSI Representation Learning for Massive MIMO Positioning](https://mlsanity.com/paper/2204.12796/) (2022)
- [Resource-aware Deep Learning for Wireless Fingerprinting Localization](https://mlsanity.com/paper/2211.01759/) (2022)
- [Self-Supervised Learning for User Localization](https://mlsanity.com/paper/2404.15370/) (2024)

## Abstract (arXiv)

Multiple-input multiple-output (MIMO) is an enabling technology to meet the growing demand for faster and more reliable communications in wireless networks with a large number of terminals, but it can also be applied for position estimation of a terminal exploiting multipath propagation from multiple antennas. In this paper, we investigate new convolutional neural network (CNN) structures for exploiting MIMO-based channel state information (CSI) to improve indoor positioning. We evaluate and compare the performance of three variants of the proposed CNN structure to five NN structures proposed in the scientific literature using the same sets of training-evaluation data. The results demonstrate that the proposed residual convolutional NN structure improves the accuracy of position estimation and keeps the total number of weights lower than the published NN structures. The proposed CNN structure yields from 2cm to 10cm better position accuracy than known NN structures used as a reference.