# Optimal preprocessing of WiFi CSI for sensing applications

arXiv: https://arxiv.org/abs/2307.12126 · HTML: https://mlsanity.com/paper/2307.12126/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2023.

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

- [Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios](https://mlsanity.com/paper/2511.14076/) (2025)
- [CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics](https://mlsanity.com/paper/2404.19725/) (2024)
- [SoK: Security Evaluation of Wi-Fi CSI Biometrics: Attacks, Metrics, and Open Challenges](https://mlsanity.com/paper/2511.11381/) (2025)
- [Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data](https://mlsanity.com/paper/2603.11858/) (2026)
- [Scene-Adaptive Continual Learning for CSI-based Human Activity Recognition with Mixture of Experts](https://mlsanity.com/paper/2605.06447/) (2026)
- [Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition](https://mlsanity.com/paper/2609.04271/) (2026)
- [WiSPER: Pose-Supervised Predictive and Residual Flow Refinement For Multi-Person 3D Pose Estimation With WiFi CSI](https://mlsanity.com/paper/2610.07025/) (2026)

## Abstract (arXiv)

Due to its ubiquitous and contact-free nature, the use of WiFi infrastructure for performing sensing tasks has tremendous potential. However, the channel state information (CSI) measured by a WiFi receiver suffers from errors in both its gain and phase, which can significantly hinder sensing tasks. By analyzing these errors from different WiFi receivers, a mathematical model for these gain and phase errors is developed in this work. Based on these models, several theoretically justified preprocessing algorithms for correcting such errors at a receiver and, thus, obtaining clean CSI are presented. Simulation results show that at typical system parameters, the developed algorithms for cleaning CSI can reduce noise by 40% and 200%, respectively, compared to baseline methods for gain correction and phase correction, without significantly impacting computational cost. The superiority of the proposed methods is also validated in a real-world test bed for respiration rate monitoring (an example sensing task), where they improve the estimation signal-to-noise ratio by 20% compared to baseline methods.