| --- |
| 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} |
| } |
| ``` |
|
|
