README.md

---
pretty_name: Scene2Wave
license: other
license_name: scene2wave-layered-license-notice
license_link: https://huggingface.co/datasets/mfzheng/Scene2Wave/blob/main/LICENSE
task_categories:
  - feature-extraction
size_categories:
  - n<1K
tags:
  - wireless
  - 6g
  - multimodal
  - channel-impulse-response
  - ray-tracing
  - autonomous-driving
  - lidar
  - radar
  - carla
  - sionna
---

# Scene2Wave

Scene2Wave is a synchronized multimodal wireless-channel dataset generated
with CARLA and Sionna RT. Version 1.0.1 contains 100 formally validated
samples spanning four CARLA Towns, five motion states, four radio profiles,
multi-view cameras, Birdview, LiDAR, Radar, IMU, GNSS, poses, path-level CIR,
and CSI.

The Scene2Wave Project Content rightsholder is **Pengcheng Laboratory**. Based
on the current generation configuration, the
dataset is known to use official CARLA assets and the official Sionna software
stack; no separately sourced scene assets have been identified.

## Authors

Mengfan Zheng, Liwen Jing, Tingting Yang, Li Sun, Yuxuan Shi, Ping Zhang,
Leiyang Xu, and Jianjun Chen.

## Dataset summary

| Property | Value |
| --- | --- |
| Formal samples | 100 |
| Towns | Town01, Town02, Town03, Town15; 25 samples each |
| Motion states | static, 10, 20, 40, 60 km/h; 20 samples each |
| Profiles | core 40, multipath 20, scattering mild 20, scattering medium 20 |
| Carrier frequency | 3.7 GHz |
| Bandwidth | 15.36 MHz |
| Subcarriers | 512 at 30 kHz spacing |
| CSI sampling rate | 2 kHz |
| Nominal sample duration | 1 second |
| CARLA | 0.9.16 |
| Sionna | 0.19.2 |

## Propagation profiles within each motion state

Each motion state (`static`, `10kmh`, `20kmh`, `40kmh`, or `60kmh`) contains
20 samples: five distinct CARLA runs from each of the four Towns. Within every
Town × motion-state bucket, those five runs are allocated as two `core` and
one each of `multipath`, `scattering_mild`, and `scattering_medium`. Thus each
motion state contains 8/4/4/4 samples respectively, and the complete release
contains 40/20/20/20.

All four profiles enable LOS, specular reflection, diffraction, and edge
diffraction. `num_samples` below is the Sionna ray-launching budget per source,
not the number of dataset examples. `max_depth` is the maximum interaction
depth used by the path solver.

| Profile | Per Town × state | Per state | `num_samples` | `max_depth` | Scattering | Material profile / scale | `scat_keep_prob` |
| --- | ---: | ---: | ---: | ---: | --- | --- | ---: |
| `core` | 2 | 8 | 1,000,000 | 2 | disabled | none / inactive | inactive |
| `multipath` | 1 | 4 | 2,000,000 | 3 | disabled | none / inactive | inactive |
| `scattering_mild` | 1 | 4 | 2,000,000 | 3 | enabled | `urban_37ghz` / 0.30 | 0.0002 |
| `scattering_medium` | 1 | 4 | 2,000,000 | 3 | enabled | `urban_37ghz` / 0.60 | 0.0005 |

The profiles have the following intended meanings:

- **`core`** is the baseline propagation setting. Its shallower interaction
  depth and smaller ray budget emphasize dominant LOS, specular, and
  diffracted components while keeping the baseline representative and
  computationally bounded.
- **`multipath`** disables diffuse scattering like `core`, but doubles the ray
  budget and raises the interaction depth from 2 to 3. It is intended to
  retain more higher-order reflected/diffracted paths and represent a denser
  non-scattering multipath condition.
- **`scattering_mild`** uses the multipath solver budget and enables the
  `urban_37ghz` material-scattering model at scale 0.30. It introduces a
  moderate diffuse component for robustness and material-scattering
  sensitivity studies.
- **`scattering_medium`** raises the same material-scattering scale to 0.60
  and increases scattering-path retention. It represents the more difficult
  diffuse-scattering condition included in the main training distribution.

`material_scattering_scale` is a relative multiplier applied to the configured
urban material-scattering profile; it is not a percentage of total received
power. Likewise, `scat_keep_prob` controls stochastic retention of candidate
scattering paths for tractable simulation and is not the physical probability
that a surface scatters.

These are independently generated `main_training` samples, not four RF
reruns of identical CARLA geometry. Profile comparisons should therefore use
stratified or aggregate analysis across Town and motion state; a difference
between two individual samples must not be attributed solely to the profile.

## Sensor suite and acquisition configuration

The CAV is a CARLA `vehicle.tesla.model3`. The roadside unit (RSU) is mounted
5 m above the sampled road surface. The following table records the formal
release configuration; counts and rates are per sample unless stated
otherwise.

| Platform / modality | Configuration | Nominal output |
| --- | --- | --- |
| CAV RGB cameras | 4 views: front, rear, right, left; 1280 × 720; 110° horizontal FOV; camera height 2.4 m | 20 Hz, 20 frames/view |
| CAV depth cameras | 4 views co-located with the RGB cameras; 1280 × 720; 110° horizontal FOV | 20 Hz, 20 frames/view |
| CAV LiDAR | 128 channels; 120 m range; 360° horizontal FOV; −10° to +90° vertical FOV; mounted at 2.5 m | 20 Hz, target 30,000 points/frame |
| CAV IMU | acceleration noise std. dev. 0.1 m/s²/axis; gyroscope noise std. dev. 0.002 rad/s/axis; gyro bias 0.001 rad/s/axis; seed 42 | 100 Hz, 100 records |
| CAV GNSS | latitude, longitude, and altitude noise std. dev. set to zero | 10 Hz, 10 records |
| RSU RGB cameras | 4 cardinal views: east, north, west, south; 1280 × 720; 110° horizontal FOV | 20 Hz, 20 frames/view |
| RSU depth cameras | 4 views co-located with the RSU RGB cameras; 1280 × 720; 110° horizontal FOV | 20 Hz, 20 frames/view |
| RSU LiDAR | 128 channels; 120 m range; 360° horizontal FOV; −50° to +50° vertical FOV | 20 Hz, target 30,000 points/frame |
| RSU Radar | 100 m range; 120° horizontal × 30° vertical FOV; yaw chosen to face the road | 20 Hz, 10,000-point ray budget/frame; stored detections vary by scene |
| Birdview RGB | top-down camera at 130 m; 1280 × 1280; 90° FOV; yaw 0° | 20 Hz, 20 frames |

The 1-second recorded interval begins after a 0.3-second simulator warm-up.
CARLA geometry/pose and CIR/CSI use a 2 kHz clock (2,000 samples), while the
physical sensors retain the native rates above. Streams are therefore
**time-aligned but not rate-equal**. Use each archive's
`carla/alignment_index.json`: camera, LiDAR, Radar, and other sensor records
are mapped to the nearest 2 kHz geometry/CSI frame. Do not assume that ordinal
file indices from two modalities identify the same timestamp.

The LiDAR point counts are generation targets rather than a promise that every
serialized cloud has exactly that size. Similarly, the Radar setting is a ray
budget; CARLA writes only returned detections, so the number of stored Radar
points depends on scene content. Per-frame poses and timestamps are stored
with the sensor data, and the full machine-readable release policy is in
`metadata/generation_config.yaml`.

Every formal sample passed CARLA trajectory and collision qualification before
wireless ray tracing. The release contains no candidate samples, visual QA
panels, videos, logs, cache keys, or editable simulator assets.

## Packaged layout

To keep the Hub repository usable, each sample is distributed as one
deterministic, uncompressed tar archive instead of hundreds of thousands of
small files:

```text
Scene2Wave/
├── README.md
├── LICENSE
├── ATTRIBUTION.md
├── THIRD_PARTY_NOTICES.md
├── CITATION.cff
├── DATA_PROVENANCE.md
├── dataset_info.json
├── schema.md
├── checksums.sha256
├── metadata/
│   ├── samples.jsonl
│   ├── source_samples.jsonl
│   ├── json_schema.json
│   ├── generation_config.yaml
│   └── raw_dataset_schema.md
└── data/main_training/<Town>/<state>/<profile>/<sample_id>.tar
```

Each tar has `sample_metadata.json`, `carla/`, and `sionna/` at its member
root. `metadata/samples.jsonl` adds `archive_path`, `archive_bytes`, and
`archive_sha256` to the public sample record. `source_samples.jsonl` preserves
the original unpacked-tree index.

## Load one sample without extracting the full dataset

```python
import io
import json
import tarfile
from pathlib import Path

import numpy as np

root = Path("Scene2Wave")
records = [
    json.loads(line)
    for line in (root / "metadata/samples.jsonl").read_text().splitlines()
    if line.strip()
]
record = records[0]

with tarfile.open(root / record["archive_path"], mode="r:") as archive:
    member = archive.extractfile("./sample_metadata.json")
    metadata = json.load(member)

    npz_name = next(
        name for name in archive.getnames()
        if name.startswith("./sionna/") and name.endswith("_paths.npz")
    )
    payload = archive.extractfile(npz_name).read()
    with np.load(io.BytesIO(payload), allow_pickle=False) as arrays:
        print(record["sample_id"], arrays.files)

print(metadata["radio"]["carrier_frequency_hz"])
```

For repeated access, extract only the required sample:

```bash
mkdir sample
tar -xf data/main_training/Town01/10kmh/core/Req_Town01_dynamic_10kmh_t001_rfmain.tar -C sample
```

See `schema.md` for archive handling and `metadata/raw_dataset_schema.md` for
the complete sensor, alignment, radar, and wireless field reference.

## Integrity

`checksums.sha256` covers every sample archive and all release metadata:

```bash
sha256sum --check checksums.sha256
```

## Intended uses

- multimodal wireless-channel representation learning;
- CIR/CSI estimation and prediction;
- perception-to-channel feature extraction;
- synchronization, fusion, and robustness research;
- controlled comparisons across Town, speed, and propagation profile.

## Limitations

- This is simulated data and does not reproduce every real sensor, material,
  traffic, weather, hardware, calibration, or regulatory condition.
- Only four CARLA Towns and the documented RF configurations are represented.
- Dataset splits must avoid leakage between closely related scenarios when
  evaluating generalization.
- Empty or outage links require explicit handling even though the formal
  release records valid CIR for all indexed samples.
- Users remain responsible for validating conclusions against real-world data.

## License and third-party materials

This repository uses a layered license. To the extent Pengcheng Laboratory
owns the applicable rights, Scene2Wave Project Content is licensed under CC BY
4.0. CARLA-derived elements and other third-party materials retain their
respective terms. Read `LICENSE`, `ATTRIBUTION.md`, and
`THIRD_PARTY_NOTICES.md`; the Hugging Face metadata therefore uses
`license: other` rather than applying one license to every layer.

## Citation

Until a dataset paper or DOI is assigned, cite this release using the metadata
in `CITATION.cff` and include the repository URL and version. Preserve the
author order listed above.