# Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks

arXiv: https://arxiv.org/abs/2212.13930 · HTML: https://mlsanity.com/paper/2212.13930/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2022.

## Built on by (most cited first, 5 of 5)

- [WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing](https://mlsanity.com/paper/2402.09430/) (2024)
- [CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision](https://mlsanity.com/paper/2605.14171/) (2026)
- [A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels](https://mlsanity.com/paper/2305.03170/) (2023)
- [Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals](https://mlsanity.com/paper/2604.02603/) (2026)
- [Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition](https://mlsanity.com/paper/2609.04271/) (2026)

## Abstract (arXiv)

As Wi-Fi becomes ubiquitous in public and private spaces, it becomes natural to leverage its intrinsic ability to sense the surrounding environment to implement groundbreaking wireless sensing applications such as human presence detection, activity recognition, and object tracking. For this reason, the IEEE 802.11bf Task Group is defining the appropriate modifications to existing Wi-Fi standards to enhance sensing capabilities through 802.11-compliant devices. However, the new standard is expected to leave the specific sensing algorithms open to implementation. To fill this gap, this article explores the practical implications of integrating sensing into Wi-Fi networks. We provide an overview of the physical and medium access control layers sensing enablers, together with the application layer perspective. We analyze the impact of communication parameters on sensing performance and detail the main research challenges. To make our evaluation replicable, we pledge to release all of our dataset and code to the community.