---
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](https://github.com/wineslab/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](http://hdl.handle.net/2047/D20410052) |
| `Tx_12-beams_*.h5` | 12 TX beams, 3 gain values | [D20410075](http://hdl.handle.net/2047/D20410075) |

For the original DeepBeam documentation and full dataset suite, refer to:  
📄 [http://hdl.handle.net/2047/D20410105](http://hdl.handle.net/2047/D20410105)  
💻 [https://github.com/wineslab/deepbeam](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 `.h5` file** 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:

```bibtex
@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}
}
```