# A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2205.08068 · HTML: https://mlsanity.com/paper/2205.08068/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 17 May 2022.

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

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning](https://mlsanity.com/paper/2107.01192/) (2021)
- [Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2112.00654/) (2021)
- [QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2104.07521/) (2021)
- [OpenCSI: An Open-Source Dataset for Indoor Localization Using CSI-Based Fingerprinting](https://mlsanity.com/paper/2104.07963/) (2021)

## Built on by (most cited first, 2 of 2)

- [Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios](https://mlsanity.com/paper/2511.14076/) (2025)
- [IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing](https://mlsanity.com/paper/2609.06131/) (2026)

## Abstract (arXiv)

Modern indoor localization techniques are essential to overcome the weak GPS coverage in indoor environments. Recently, considerable progress has been made in Channel State Information (CSI) based indoor localization with signal fingerprints. However, CSI signal patterns can be complicated in the large and highly dynamic indoor spaces with complex interiors, thus a solution for solving this issue is urgently needed to expand the applications of CSI to a broader indoor space. In this paper, we propose an end-to-end solution including data collection, pattern clustering, denoising, calibration and a lightweight one-dimensional convolutional neural network (1D CNN) model with CSI fingerprinting to tackle this problem. We have also created and plan to open source a CSI dataset with a large amount of data collected across complex indoor environments at Colorado State University. Experiments indicate that our approach achieves up to 68.5% improved performance (mean distance error) with minimal number of parameters, compared to the best-known deep machine learning and CSI-based indoor localization works.