Datasets:
license: other
task_categories:
- audio-classification
language:
- en
- it
tags:
- mmwave
- beam-management
- deepbeam
- photonic
- neural-network
- wireless
- IQ-samples
pretty_name: PhotonicDeepBeam Dataset
size_categories:
- 100B<n<1T
PhotonicDeepBeam Dataset
This repository hosts the datasets used in the QuantBeam project — an adaptation of the DeepBeam architecture towards Photonic-Aware Neural Networks (PANN) for millimeter-wave (mmWave) beam classification.
⚠️ The datasets are NOT ours. They are the original experimental datasets collected and published by the DeepBeam / WiNES Lab team (Polese, Restuccia, Melodia — Northeastern University). We re-host them here solely to facilitate reproducibility of our project. All credit and intellectual property belongs to the original authors.
Original Dataset Sources (DeepBeam)
| Dataset | Description | Original Link |
|---|---|---|
Tx_5-beams.h5 |
5 TX beams, 3 gain values | D20410052 |
Tx_12-beams_*.h5 |
12 TX beams, 3 gain values | D20410075 |
For the original DeepBeam documentation and full dataset suite, refer to:
📄 http://hdl.handle.net/2047/D20410105
💻 https://github.com/wineslab/deepbeam
Repository Contents
PhotonicDeepBeam-dataset/
├── Dataset/
│ ├── Tx_5-beams.h5 # 5-beam dataset (Pi-Radio TXB)
│ ├── Tx_12-beams_<config>.h5 # 12-beam dataset (single config used in our work)
│ └── Indexes/
│ ├── Tx_5-beams_5percent/ # Pre-generated split indexes (seed 42–46, 5% undersampling)
│ ├── Tx_5-beams_20percent/ # Pre-generated split indexes (seed 42–46, 20% undersampling)
│ ├── Tx_5-beams_100percent/ # Pre-generated split indexes (seed 42–46, full dataset)
│ └── Tx_12-beams_20percent/ # Pre-generated split indexes (seed 42–46, 20% undersampling)
└── Modelli addestrati/
├── DNN/
│ ├── 5 beams/ # Trained DNN weights for 5-beam task
│ └── 12 beams/ # Trained DNN weights for 12-beam task
└── PANN/
├── 5 beams/ # Trained PANN weights (various configs)
└── 12 beams/ # Trained PANN weights (various configs)
Index files (.pkl)
Pre-computed index files ensure that the exact same train/validation/test split is used across devices and runs. Each .pkl file contains a list [train_indexes, valid_indexes, test_indexes] where each element is a list of frame indices into the corresponding .h5 file.
Filename convention:i_<undersample%>_<seed>_<train%>_<valid%>_<test%>.pkl
Example: i_20_42_70_15_15.pkl → 20% undersampling, seed 42, 70/15/15 split.
Dataset Description
The data consists of real-world mmWave waveform measurements collected using the DeepBeam experimental testbeds. Signals are represented as In-Phase and Quadrature (I/Q) samples of the raw baseband waveform from the receiver RF chain.
5-beam dataset
- Testbed: Pi-Radio TXB
- TX beams: 5
- Transmitter gain values: 3
- Blocks per frame: 100 (5 used per sample)
- Samples per block: 512
- Frames per (beam, gain) pair: 10,000
- Input tensor shape:
(batch, 5, 512, 2)— 5 blocks × 512 samples × [I, Q]
12-beam dataset
- Testbed: Multi-RF-chain
- TX beams: 12
- Receiver gain values: 3 (40 dB, 50 dB, 60 dB → SNR range ≈ −15 dB to +20 dB)
- Blocks per frame: 15 (5 used per sample)
- Samples per block: 2048
- Frames per (beam, gain) pair: 10,000
- Input tensor shape:
(batch, 5, 2048, 2)— 5 blocks × 2048 samples × [I, Q]
Note: In our project we used only a single
.h5file for the 12-beam task (the simplest available configuration) and trained on 20% of the data due to hardware constraints.
How to Use in the QuantBeam Notebook
The notebook (PhotonicDeepBeam_Notebook.ipynb) handles dataset loading automatically. To use this HuggingFace repository, initialize the storage using the Persistent_Storage
class like in the notebook.
Citation
Please cite the original DeepBeam paper when using this data:
@inproceedings{polese2021deepbeam,
author = {Polese, Michele and Restuccia, Francesco and Melodia, Tomaso},
title = {DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks},
booktitle = {Proceedings of ACM MobiHoc},
year = {2021}
}