Datasets:

Aadharsh
/

RepresentationLearning-dataset

pretty_name: RepresentationLearning ORACLE triplets
tags:
  - wireless
  - rf-fingerprinting
  - channel-estimation
  - cfo-estimation
  - iq-samples
size_categories:
  - 100K<n<1M

RepresentationLearning dataset

Preprocessed WiFi packets for three wireless tasks, used to train and attack the multi-task representations in RepresentationLearning. Pretrained models and the run registry are in Aadharsh/RepresentationLearning-models.

The packets are derived from the ORACLE RF fingerprinting dataset (16 USRP X310 transmitters recorded at 11 distances, 2–62 ft).

Files

File	Size	Contents
`OracleDatasetProcessed-arranged.tar.gz`	3.3 GB	416,244 `.mat` files (see below)
`rf_partition_dict_0.5.pkl`	5.8 MB	train/val/test split and CFO normalisation constants

Layout

tar xzf OracleDatasetProcessed-arranged.tar.gz

The archive has a doubled top-level directory. The .mat files are in OracleDatasetProcessed-arranged/OracleDatasetProcessed-arranged/; point DATA_BASE_PATH there.

One sample is a triplet sharing the suffix _run1_Radio<r>_<d>ft_<i>.mat:

Prefix	Variables	Task
`RFfingerprinting`	`Packet` (4000×1 complex), `Radio` (label, `"Radio0"`–`"Radio15"`)	RF fingerprinting (16 classes)
`CFOEstimation`	`LSTF` (160×1 complex), `CFO` (scalar, Hz)	CFO estimation
`ChannelEstimation`	`LLTF` (160×1 complex), `EstChannnel` (52×1 complex)	Channel estimation

EstChannnel really is spelled with three n's. The archive also contains 24,525 CFOEstimation files without RF/channel partners; no split references them.

Split

rf_partition_dict_0.5.pkl is a dict:

Key	Value
`train` / `val` / `test`	lists of `RFfingerprinting_*.mat` filenames: 91,323 / 13,070 / 26,180
`mean_cfo`, `std_cfo`, `max_cfo`	CFO statistics of the training split, used to normalise CFO targets

Filenames are relative to DATA_BASE_PATH. The registry's test metrics for runs from July 2025 onward were computed on this split, so use it unchanged when comparing with the registry.

Loading

git clone https://github.com/nasimsoltani/RepresentationLearning.git
cd RepresentationLearning && cp .env.example .env   # set DATA_BASE_PATH and PKL_FILE_PATH
python code/rep_lr/inference.py                     # evaluate the best released model

Each input is normalised to unit RMS and split into I/Q channels by code/rep_lr/py_datasets.py.

Citation

The ORACLE authors ask that any publication using their data cite:

@inproceedings{sankhe2019oracle,
  title={ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks},
  author={Sankhe, Kunal and Belgiovine, Mauro and Zhou, Fan and Riyaz, Shamnaz and Ioannidis, Stratis and Chowdhury, Kaushik},
  booktitle={IEEE INFOCOM 2019 - IEEE Conference on Computer Communications},
  pages={370--378},
  year={2019}
}