# Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data

arXiv: https://arxiv.org/abs/2603.11858 · HTML: https://mlsanity.com/paper/2603.11858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 20 of 20)

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- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking](https://mlsanity.com/paper/2307.16847/) (2023)
- [WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing](https://mlsanity.com/paper/2402.09430/) (2024)
- [AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning](https://mlsanity.com/paper/2205.01629/) (2022)
- [Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing](https://mlsanity.com/paper/2403.12400/) (2024)
- [A Tutorial-cum-Survey on Self-Supervised Learning for Wi-Fi Sensing: Trends, Challenges, and Outlook](https://mlsanity.com/paper/2506.12052/) (2025)
- [RFBoost: Understanding and Boosting Deep WiFi Sensing via Physical Data Augmentation](https://mlsanity.com/paper/2410.07230/) (2024)
- [Optimal preprocessing of WiFi CSI for sensing applications](https://mlsanity.com/paper/2307.12126/) (2023)
- [Self-Supervised WiFi-Based Activity Recognition](https://mlsanity.com/paper/2104.09072/) (2021)
- [Deep Learning based Positioning with Multi-task Learning and Uncertainty-based Fusion](https://mlsanity.com/paper/2403.08565/) (2024)
- [Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition](https://mlsanity.com/paper/2401.00964/) (2024)
- [Through-Wall Imaging based on WiFi Channel State Information](https://mlsanity.com/paper/2401.17417/) (2024)

## Abstract (arXiv)

We propose a WiFi Channel State Information (CSI) sensing framework for multi-station deployments that addresses two fundamental challenges in practical CSI sensing: station-wise feature missingness and limited labeled data. Feature missingness is commonly handled by resampling unevenly spaced CSI measurements or by reconstructing missing samples, while label scarcity is mitigated by data augmentation or self-supervised representation learning. However, these techniques are typically developed in isolation and do not jointly address long-term, structured station unavailability together with label scarcity. To bridge this gap, we explicitly incorporate station unavailability into both representation learning and downstream model training. Specifically, we adapt cross-modal self-supervised learning (CroSSL), a representation learning framework originally designed for time-series sensory data, to multi-station CSI sensing in order to learn representations that are inherently invariant to station-wise feature missingness from unlabeled data. Furthermore, we introduce Station-wise Masking Augmentation (SMA) during downstream model training, which exposes the model to realistic station unavailability patterns under limited labeled data. Our experiments show that neither missingness-invariant pre-training nor station-wise augmentation alone is sufficient; their combination is essential to achieve robust performance under both station-wise feature missingness and label scarcity. The proposed framework provides a practical and robust foundation for multi-station WiFi CSI sensing in real-world deployments.