# A Tutorial-cum-Survey on Self-Supervised Learning for Wi-Fi Sensing: Trends, Challenges, and Outlook

arXiv: https://arxiv.org/abs/2506.12052 · HTML: https://mlsanity.com/paper/2506.12052/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2025.

## Built on by (most cited first, 11 of 11)

- [AM-FM: A Foundation Model for Ambient Intelligence Through WiFi](https://mlsanity.com/paper/2602.11200/) (2026)
- [DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi](https://mlsanity.com/paper/2507.12132/) (2025)
- [CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision](https://mlsanity.com/paper/2605.14171/) (2026)
- [Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition](https://mlsanity.com/paper/2509.15129/) (2025)
- [CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing](https://mlsanity.com/paper/2512.04723/) (2025)
- [A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles](https://mlsanity.com/paper/2508.00917/) (2025)
- [Parameter-Efficient Domain Adaption for CSI Crowd-Counting via Self-Supervised Learning with Adapter Modules](https://mlsanity.com/paper/2601.02203/) (2026)
- [Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data](https://mlsanity.com/paper/2603.11858/) (2026)
- [MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2605.01369/) (2026)
- [WiFuse: An Attention Mechanism for Human Activity Recognition using Fused CSI Amplitude and Delay-Doppler Channel Features](https://mlsanity.com/paper/2608.00642/) (2026)
- [DoRF++: Spherical Representation Learning over Doppler Radiance Fields for Robust Wi-Fi Sensing](https://mlsanity.com/paper/2608.08381/) (2026)

## Abstract (arXiv)

Wi-Fi technology has evolved from simple communication routers to sensing devices. Wi-Fi sensing leverages conventional Wi-Fi transmissions to extract and analyze channel state information (CSI) for applications like proximity detection, occupancy detection, activity recognition, and health monitoring. By leveraging existing infrastructure, Wi-Fi sensing offers a privacy-preserving, non-intrusive, and cost-effective solution which, unlike cameras, is not sensitive to lighting conditions. Beginning with a comprehensive review of the Wi-Fi standardization activities, this tutorial-cum-survey first introduces fundamental concepts related to Wi-Fi CSI, outlines the CSI measurement methods, and examines the impact of mobile objects on CSI. The mechanics of a simplified testbed for CSI extraction are also described. Then, we present a qualitative comparison of the existing Wi-Fi sensing datasets, their specifications, and pin-point their shortcomings. Next, a variety of preprocessing techniques are discussed that are beneficial for feature extraction and explainability of machine learning (ML) algorithms. We then provide a qualitative review of recent ML approaches in the domain of Wi-Fi sensing and present the significance of self-supervised learning (SSL) in that context. Specifically, the mechanics of contrastive and non-contrastive learning solutions is elaborated in detail and a quantitative comparative analysis is presented in terms of classification accuracy. Finally, the article concludes by highlighting emerging technologies that can be leveraged to enhance the performance of Wi-Fi sensing and opportunities for further research in this domain