# CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

arXiv: https://arxiv.org/abs/2205.05775 · HTML: https://mlsanity.com/paper/2205.05775/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [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)
- [Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems](https://mlsanity.com/paper/1910.12378/) (2019)
- [Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network](https://mlsanity.com/paper/2102.03130/) (2021)
- [MaMIMO CSI-based positioning using CNNs: Peeking inside the black box](https://mlsanity.com/paper/2003.04581/) (2020)
- [A CNN-LSTM Quantifier for Single Access Point CSI Indoor Localization](https://mlsanity.com/paper/2005.06394/) (2020)
- [Sensor-Aided Learning for Wi-Fi Positioning with Beacon Channel State Information](https://mlsanity.com/paper/2007.06204/) (2020)

## Built on by (most cited first, 7 of 7)

- [Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning](https://mlsanity.com/paper/2410.20680/) (2024)
- [Neural 5G Indoor Localization with IMU Supervision](https://mlsanity.com/paper/2402.09948/) (2024)
- [Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning](https://mlsanity.com/paper/2405.11816/) (2024)
- [Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels](https://mlsanity.com/paper/2505.13055/) (2025)
- [Reliable Wireless Indoor Localization via Cross-Validated Prediction-Powered Calibration](https://mlsanity.com/paper/2507.20268/) (2025)
- [Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization](https://mlsanity.com/paper/2408.06452/) (2024)
- [CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models](https://mlsanity.com/paper/2607.14975/) (2026)

## Abstract (arXiv)

Deep learning has been widely adopted for channel state information (CSI)-fingerprinting indoor localization systems. These systems usually consist of two main parts, i.e., a positioning network that learns the mapping from high-dimensional CSI to physical locations and a tracking system that utilizes historical CSI to reduce the positioning error. This paper presents a new localization system with high accuracy and generality. On the one hand, the receptive field of the existing convolutional neural network (CNN)-based positioning networks is limited, restricting their performance as useful information in CSI is not explored thoroughly. As a solution, we propose a novel attention-augmented residual CNN to utilize the local information and global context in CSI exhaustively. On the other hand, considering the generality of a tracking system, we decouple the tracking system from the CSI environments so that one tracking system for all environments becomes possible. Specifically, we remodel the tracking problem as a denoising task and solve it with deep trajectory prior. Furthermore, we investigate how the precision difference of inertial measurement units will adversely affect the tracking performance and adopt plug-and-play to solve the precision difference problem. Experiments show the superiority of our methods over existing approaches in performance and generality improvement.