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Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices

Khandaker Foysal Haque, Francesca Meneghello, Francesco Restuccia

TL;DR

This work addresses the challenge of extracting usable beamforming feedback information from over-the-air frames by introducing Wi-BFI, the first open-source, Python-based tool that retrieves 802.11ac/ax BFAs and reconstructs the beamforming feedback information (BFI) across 20–160 MHz bandwidths and arbitrary network configurations without access to beamformers or beamformees. It leverages the channel sounding process and the angle-based BFIs (phi and psi) to reconstruct the tilde{V}_k matrices that capture the compressed CFR information, enabling real-time or offline analysis and multi-device captures. The paper demonstrates a sensing application where a CNN-based classifier achieves up to 99.28% accuracy in activity recognition by exploiting BFI-derived features, underscoring the practical value of BFIs for passive sensing, radio fingerprinting, and network optimization. By providing a unified, open-source tool that works in-the-wild across standards and bandwidths, the work lowers barriers to broad research and potential deployments in Wi-Fi sensing and fingerprinting domains.

Abstract

Recently, researchers have shown that the beamforming feedback angles (BFAs) used for Wi-Fi multiple-input multiple-output (MIMO) operations can be effectively leveraged as a proxy of the channel frequency response (CFR) for different purposes. Examples are passive human activity recognition and device fingerprinting. However, even though the BFAs report frames are sent in clear text, there is not yet a unified open-source tool to extract and decode the BFAs from the frames. To fill this gap, we developed Wi-BFI, the first tool that allows retrieving Wi-Fi BFAs and reconstructing the beamforming feedback information (BFI) - a compressed representation of the CFR - from the BFAs frames captured over the air. The tool supports BFAs extraction within both IEEE 802.11ac and 802.11ax networks operating on radio channels with 160/80/40/20 MHz bandwidth. Both multi-user and single-user MIMO feedback can be decoded through Wi-BFI. The tool supports real-time and offline extraction and storage of BFAs and BFI. The real-time mode also includes a visual representation of the channel state that continuously updates based on the collected data. Wi-BFI code is open source and the tool is also available as a pip package.

Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices

TL;DR

This work addresses the challenge of extracting usable beamforming feedback information from over-the-air frames by introducing Wi-BFI, the first open-source, Python-based tool that retrieves 802.11ac/ax BFAs and reconstructs the beamforming feedback information (BFI) across 20–160 MHz bandwidths and arbitrary network configurations without access to beamformers or beamformees. It leverages the channel sounding process and the angle-based BFIs (phi and psi) to reconstruct the tilde{V}_k matrices that capture the compressed CFR information, enabling real-time or offline analysis and multi-device captures. The paper demonstrates a sensing application where a CNN-based classifier achieves up to 99.28% accuracy in activity recognition by exploiting BFI-derived features, underscoring the practical value of BFIs for passive sensing, radio fingerprinting, and network optimization. By providing a unified, open-source tool that works in-the-wild across standards and bandwidths, the work lowers barriers to broad research and potential deployments in Wi-Fi sensing and fingerprinting domains.

Abstract

Recently, researchers have shown that the beamforming feedback angles (BFAs) used for Wi-Fi multiple-input multiple-output (MIMO) operations can be effectively leveraged as a proxy of the channel frequency response (CFR) for different purposes. Examples are passive human activity recognition and device fingerprinting. However, even though the BFAs report frames are sent in clear text, there is not yet a unified open-source tool to extract and decode the BFAs from the frames. To fill this gap, we developed Wi-BFI, the first tool that allows retrieving Wi-Fi BFAs and reconstructing the beamforming feedback information (BFI) - a compressed representation of the CFR - from the BFAs frames captured over the air. The tool supports BFAs extraction within both IEEE 802.11ac and 802.11ax networks operating on radio channels with 160/80/40/20 MHz bandwidth. Both multi-user and single-user MIMO feedback can be decoded through Wi-BFI. The tool supports real-time and offline extraction and storage of BFAs and BFI. The real-time mode also includes a visual representation of the channel state that continuously updates based on the collected data. Wi-BFI code is open source and the tool is also available as a pip package.
Paper Structure (9 sections, 6 equations, 13 figures, 1 table, 1 algorithm)

This paper contains 9 sections, 6 equations, 13 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Architecture of Wi-BFI
  3. Wi-BFI Operation Principle
  4. Wi-BFI Use case: Sensing with BFAs
  5. Learning Architecture
  6. Experimental Setup
  7. Sensing Performance with
  8. Related Work
  9. Conclusions and Remarks

Figures (13)

  • Figure 1: Wi-BFI overview.
  • Figure 2: Wi-BFI -- overall system architecture.
  • Figure 3: IEEE 802.11ac/ax BFI report frame structure.
  • Figure 4: Main steps of the Wi-BFI tool.
  • Figure 5: ($\phi_{11}$ and $\phi_{21}$) of each sounded sub-channel of a $\mathbf{4\times2}$ IEEE 802.11ax system at 160 MHz.
  • ...and 8 more figures