| | --- |
| | license: odbl |
| | --- |
| | # OpenPathNet Dataset |
| |
|
| | This README describes the **OpenPathNet** dataset (the release referred to as **Link 1** in the [OpenPathNet project](https://github.com/liu-lz/OpenPathNet) documentation). The dataset is generated by the OpenPathNet toolchain from real-world **Miami** and **Boston** urban areas based on OpenStreetMap (OSM), and then simulated with NVIDIA Sionna ray tracing for RF multipath propagation / channel modeling research and AI tasks. |
| |
|
| | The dataset is also **carefully cleaned** to ensure **good building coverage** in every scene. |
| |
|
| | - Project code / generator repository: <https://github.com/liu-lz/OpenPathNet> |
| | - City subsets: **Miami (806 scenes)** and **Boston (971 scenes)** (same directory layout across cities) |
| |
|
| | ## Directory Structure |
| |
|
| | ``` |
| | . |
| | ├── Miami/ |
| | │ ├── scenes/ |
| | │ │ └── scene_<lat>_<lon>/ |
| | │ │ ├── scene.xml |
| | │ │ └── mesh/ |
| | │ │ ├── building_*.ply |
| | │ │ └── ground.ply |
| | │ ├── raytracing_results/ |
| | │ │ └── scene_<lat>_<lon>/ |
| | │ │ ├── raytracing_results.csv |
| | │ │ ├── raytracing_results.pkl |
| | │ │ ├── deepmimo_format.npy |
| | │ │ ├── channel_gain_distribution.png |
| | │ │ ├── delay_distribution.png # Here “delay” refers to ToA (Time of Arrival); the latest OpenPathNet fixes this filename |
| | │ │ ├── path_type_distribution.png |
| | │ │ ├── outdoor_receivers.png |
| | │ │ └── heatmaps/ |
| | │ │ ├── azimuth_heatmap.png |
| | │ │ ├── channel_gain_heatmap.png |
| | │ │ ├── delay_heatmap.png # Here “delay” refers to ToA (Time of Arrival); the latest OpenPathNet fixes this filename |
| | │ │ └── elevation_heatmap.png |
| | │ ├── generated_scenes.txt |
| | │ └── raytracing.log |
| | ├── Boston/ |
| | │ └── ... (same structure as Miami) |
| | └── README.md |
| | ``` |
| |
|
| | ### Naming |
| |
|
| | - Each scene directory is named `scene_<lat>_<lon>`, where `<lat>` / `<lon>` are decimal latitude/longitude. |
| | - Scene folders under `scenes/` and `raytracing_results/` correspond one-to-one. |
| |
|
| | ## Files |
| |
|
| | ### generated_scenes.txt |
| | |
| | A generation manifest and metadata (tabular text). |
| | |
| | - The header records the total count, center coordinate, sampling radius, scene size, generation mode, etc. |
| | - Each (tab-separated) row includes: |
| | - Scene file path (e.g., `data\scenes\scene_...\scene.xml`) |
| | - Original / actual latitude & longitude |
| | - Generation type (e.g., `OSM`) |
| | - Attempts |
| | - Offset distance (km) |
| |
|
| | ### raytracing.log |
| |
|
| | A summary log for batched ray tracing (typically one line per scene), including runtime, number of receivers, and number of paths. |
| |
|
| | ### scenes/ |
| |
|
| | Geometry assets for each scene. |
| |
|
| | - `scene.xml`: scene description file (digital-twin / renderer-compatible format). |
| | - `mesh/`: geometry meshes (e.g., buildings and ground) in `.ply`. |
| |
|
| | ### raytracing_results/ |
| | |
| | Ray-tracing outputs and visualizations for each scene. |
| | |
| | For each receiver point in each scene, this dataset keeps and records the **top 5 paths with the highest channel gain**. The files below contain the full multipath attributes for those retained paths, including receiver location, carrier frequency, path type, channel gain, **ToA (Time of Arrival)**, and departure/arrival angles. |
| | |
| | - `raytracing_results.csv`: tabular results (easy to analyze/import). |
| | - `raytracing_results.pkl`: Python-serialized results (fast loading). |
| | - `deepmimo_format.npy`: DeepMIMO-style structured output for downstream ML pipelines. |
| | - `heatmaps/` and `*.png`: visualizations (e.g., channel gain / delay(ToA) / azimuth / elevation). |
| |
|
| | #### Data schema: raytracing_results.csv / raytracing_results.pkl |
| |
|
| | - Structure: tabular data; typically **one row = (receiver `rx_id`, one path)**, so each `rx_id` usually appears 5 times. |
| | - `raytracing_results.pkl` is a `pandas.DataFrame` with the same columns as `raytracing_results.csv`. |
| | |
| | Columns: |
| | |
| | - `rx_id`: receiver index (integer). |
| | - `type`: path type (e.g., `LoS` / `Reflected` / `Scattered`). |
| | - `channel_gain`: channel gain-related numeric value (scientific notation). |
| | - `tau`: **ToA (Time of Arrival)** in seconds. |
| | - `freq`: carrier frequency in Hz. |
| | - `rx_coord`: receiver coordinates, formatted as a string like `"[x, y, z]"`. |
| | - `phi_r`, `theta_r`: AoA azimuth / elevation angles. |
| | - `phi_t`, `theta_t`: AoD azimuth / elevation angles. |
| |
|
| | #### Data schema: deepmimo_format.npy |
| | |
| | - File content: a scalar `numpy.ndarray` with `dtype=object`; `arr.item()` yields a `dict`. |
| | - Top-level keys: |
| | - `user`: a list of length $N_{rx}$; each element corresponds to one receiver. |
| | - `location`: a list used to describe scene / coordinate system information (may vary slightly across versions/configs). |
| |
|
| | Each `user[i]` is a `dict` containing: |
| |
|
| | - `location`: `numpy.ndarray` of shape `(3,)`, receiver coordinates `[x, y, z]`. |
| | - `paths`: a `dict` containing (arrays are length 5, i.e., Top-5 paths): |
| | - `channel_gain`: `float32`, shape `(5,)` |
| | - `ToA`: `float32`, shape `(5,)` |
| | - `DoA_theta`: `float64`, shape `(5,)` |
| | - `DoA_phi`: `float64`, shape `(5,)` |
| | - `num_paths`: `int` (5 in this dataset) |
| |
|
| | ## Reproducibility / Regeneration |
| |
|
| | This dataset is generated by the OpenPathNet toolchain. For generation scripts, ray-tracing entry points, and system requirements, please refer to: |
| |
|
| | - <https://github.com/liu-lz/OpenPathNet> |
| |
|
| | ## Citation |
| |
|
| | If you use OpenPathNet in your research, please refer to the citation information in the [OpenPathNet repository documentation](https://github.com/liu-lz/OpenPathNet). |
| |
|
| | ## License & Notes |
| |
|
| | - This directory contains a dataset slice/subset. For licensing, the generator code license, and third-party data source statements (OSM, etc.), please follow the [OpenPathNet repository documentation](https://github.com/liu-lz/OpenPathNet). |
