---
license: cc-by-4.0
task_categories:
  - other
tags:
  - wireless
  - channel-estimation
  - rf
  - gaussian-splatting
  - sionna
  - massive-mimo
  - rfid
pretty_name: GSpaRC Datasets
size_categories:
  - 1K<n<10K
---

# GSpaRC Datasets

Datasets used in the paper **"GSpaRC: Gaussian Splatting for Real-time Reconstruction of RF Channels"**.

- 📄 **Paper:** [arXiv:2511.22793](https://arxiv.org/abs/2511.22793)
- 🌐 **Project website:** https://nbhavyasai.github.io/GSpaRC/
- 💻 **Code:** https://github.com/Nbhavyasai/GSpaRC-WirelessGaussianSplatting

We evaluate GSpaRC on three RF datasets. Only the **Sionna conference-room** dataset is hosted in this repository (it was generated by us). The **RFID** and **Argos** datasets are publicly available from their original sources, linked below.

---

## 1. Sionna Conference-Room Dataset (hosted here)

Indoor spectrum dataset generated with the [Sionna](https://nvlabs.github.io/sionna/) ray tracer. A single transmitter is fixed near the ceiling and 5,142 receiver positions are sampled across the room floor. Each receiver position yields one spatial spectrum image.

**Files** (under `sionna_conference_room/`):

| File | Description |
|---|---|
| `spectrum/00001.npy` … `05142.npy` | Per-position spatial spectrum (`90 × 360` magnitude image, `float32`) |
| `rx_pos.csv` | Receiver positions, columns: `x, y, z` (5,142 rows) |
| `gateway_info.yml` | Transmitter position `[0.0, 0.5, 3.0]` and orientation quaternion |

**Splits used in the paper:**
- 3,599 training positions / 1,543 test positions (random split)

**Scene:** indoor conference room, 14 m × 10 m × 4 m, with tables, chairs, glass partitions and irregular wall materials, producing rich multipath.

### Loading example

```python
import numpy as np
import pandas as pd
import yaml

# Receiver positions
rx_pos = pd.read_csv("sionna_conference_room/rx_pos.csv")  # columns: x,y,z

# Transmitter
with open("sionna_conference_room/gateway_info.yml") as f:
    info = yaml.safe_load(f)
tx_pos = info["gateway1"]["position"]  # [0.0, 0.5, 3.0]

# Spectrum at the i-th receiver position
spec = np.load(f"sionna_conference_room/spectrum/{i:05d}.npy")  # (90, 360)
```

---

## 2. RFID Spectrum Dataset (external)

Real-world dataset originally released with the **NeRF²** paper.

- **Download:** [OneDrive link](https://onedrive.live.com/?redeem=aHR0cHM6Ly8xZHJ2Lm1zL2YvYy82MGQ1MjkwOWYyYjA0YTZjL0V0Z0h1QzR0NnhwRm8xdHNYemdRUWZjQmdveXJnWmtrVndmOXdjWDhxeEpzalE%5FZT1oTWVFWFE&id=60D52909F2B04A6C%21sc87ce336ceaa46be8dd4a49f9792b4bf&cid=60D52909F2B04A6C&sb=name&sd=1) (provided by the NeRF² authors)
- **Original repo:** https://github.com/XPengZhao/NeRF2
- **Setup:** receiver fixed at 915 MHz with a 4×4 antenna array; an RFID-tag transmitter is placed at 6,123 distinct locations, each producing a spatial-spectrum image.
- **Citation:**
  > Zhao, X., An, Z., Pan, Q., & Yang, L. (2023). NeRF²: Neural Radio-Frequency Radiance Fields. *MobiCom 2023*.

We use this dataset as-is — no preprocessing changes — and report results in the paper's RFID section.

---

## 3. Argos Massive-MIMO CSI Dataset (external)

Real-world outdoor CSI dataset from the **Argos** massive-MIMO platform.

- **Download:** [OneDrive link](https://onedrive.live.com/?redeem=aHR0cHM6Ly8xZHJ2Lm1zL2YvYy82MGQ1MjkwOWYyYjA0YTZjL0V0Z0h1QzR0NnhwRm8xdHNYemdRUWZjQmdveXJnWmtrVndmOXdjWDhxeEpzalE%5FZT1oTWVFWFE&id=60D52909F2B04A6C%21s824372ce9ef9412b8647c4a8c3272508&cid=60D52909F2B04A6C&sb=name&sd=1) (mirrored on the NeRF² authors' OneDrive)
- **Original source:** https://renew.rice.edu/dataset-argos.html
- **Setup:** 64-antenna base station at 2.4 GHz; 4,000 user positions; complex CSI on 26 subcarriers.
- **Citation:**
  > Shepard, C., Yu, H., & Zhong, L. (2016). Understanding the Argos clean-slate massive MIMO platform. *Rice University Technical Report*.

For our experiments we follow the FIRE preprocessing (Liu et al., 2021) to obtain 3D receiver positions from the uplink measurements.

---

## License

- The **Sionna conference-room dataset** (this repo) is released under **CC-BY-4.0**.
- The **RFID** and **Argos** datasets are governed by their respective original licenses — please refer to the source links above.

## Citation

If you use any of these datasets together with GSpaRC, please cite:

```bibtex
@article{nukapotula2026gsparc,
  title   = {GSpaRC: Gaussian Splatting for Real-time Reconstruction of RF Channels},
  author  = {Nukapotula, Bhavya Sai and Tripathi, Rishabh and Pregler, Seth and Kalathil, Dileep and Shakkottai, Srinivas and Rappaport, Tedd},
  journal = {arXiv preprint arXiv:2511.22793},
  year    = {2026}
}
```