Meneghello, Francesca and Rossi, Michele and Restuccia, Francesco (2022) MU-MIMO Wi-Fi beamforming feedback matrices database for radio fingerprinting. [Data Collection]
Collection description
This database collects the .pcap files acquired by monitoring a private IEEE 802.11ac Wi-Fi network operating in multi-user multi-input multi-output (MU-MIMO) mode. The Wi-Fi network consists of one access point (AP) (beamformer) and two stations (STAs) (beamformees). The AP was implemented through a Gateworks GW6200 single board computer (SBC) equipped with a Compex WLE1216v5-23 IEEE 802.11ac module. Two Netgear Nighthawk X4S AC2600 routers, with 1 or 2 out of 4 antennas enabled, acted as STAs (beamformees). At the AP, 3 antennas were used to sound the channel for downlink (DL) MU-MIMO transmission mode and the STAs were served with 1 or 2 spatial streams each. For the data transmission between the AP and the STAs, we used channel 42, i.e., with carrier frequency of 5.21 GHz and 80 MHz bandwidth. The number of OFDM sub-channels sounded is 234 as the mechanism does not consider the 14 control sub-channels and the 8 pilot ones. The AP uses the quantization parameters 9 and 7 for phi and psi feedback angles, respectively. We generated UDP traffic in the DL direction to induce the AP to trigger the channel sounding mechanism, and collected the angles (phi, psi) that were sent back by the beamformees using the Wireshark network analyzed toolkit running on an off-the-shelf laptop equipped with an IEEE~802.11ac Wi-Fi card. This allows retrieving the beamforming feedback matrices associated with each sounding operation. Two datasets - namely D1 and D2 - were collected. As for the former, the STAs were deployed at different positions to generate different beam patterns and different SNR regimes. The number of enabled antennas is 2 for each beamformer and each of them is served with 2 spatial streams. Dataset D2 was collected while the AP was manually moved in the environment. Here, the number of enabled antennas and spatial streams is 1 for the first beamformee and 2 for the second. The datasets were collected in two different indoor environments. For more information about the setup, please, refer to the related publication. This dataset was used to design and assess the performance of DeepCSI presented in the article ''DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning'' by Francesca Meneghello, Michele Rossi, Francesco Restuccia. The Python source code is available at https://github.com/signetlabdei/DeepCSI. If you use this dataset, please cite our paper: @inproceedings{meneghello2022deepcsi, author = {Meneghello, Francesca and Rossi, Michele and Restuccia, Francesco}, title = {{DeepCSI: Rethinking Wi-Fi Radio FingerprintingThrough MU-MIMO CSI Feedback Deep Learning}}, booktitle = {IEEE International Conference on Distributed Computing Systems}, year = {2022} }
10.25430/researchdata.cab.unipd.it.00000623
orcid.org/0000-0002-9905-0360
