---
license: mit
language:
- en
tags:
- Wi-Fi
- CSI
- RSSI
---

---
WiFi CSI Router Reception Dataset
tags:
  - wifi
  - csi
  - esp32
  - signal-processing
  - time-series
size_categories:
  - 100K<n<1M
---

# WiFi CSI Router Reception Dataset

This repository contains a small, structured dataset of **WiFi Channel State Information (CSI)** captures recorded as text logs (`.data`). The data is organized as repeated trials (`test_XX`) for **4 class labels** (`label_00..label_03`) and **4 subjects/persons** (`id_person_01..id_person_04`), recorded simultaneously by **3 receiver devices** (`dev1..dev3`).

The “Hugging Face–ready” dataset folder is `wifi_data_set/`.

## Dataset summary

- **Modality:** WiFi CSI (I/Q samples) + per-frame metadata
- **Format:** plain text files; 1 CSI frame per line
- **CSI vector:** 128 signed integers per frame (typically interpreted as 64 complex subcarriers as interleaved I/Q pairs)
- **People (subjects):** 4 (`id_person_01..04`)
- **Labels (classes):** 4 (`label_00..03`) *(see “Label meanings” below)*
- **Trials:** 100 per (person, label)
- **Devices per trial:** 3 (`dev1`, `dev2`, `dev3`)
- **Frames per file:** 100 lines *(see “Known issues” for malformed first lines in some files)*
- **Total files:** 4 persons × 4 labels × 100 tests × 3 devices = **4,800** `.data` files
- **Total frames:** 4,800 files × 100 lines = **480,000** logged frames
- **Collection time range:** **2026-04-18 20:17:59.599 +03:00** … **2026-04-18 23:21:01.561 +03:00**
- **Typical sampling:** ~0.039–0.044 s between frames (≈ 23–28 Hz); ~4 s per trial

## Dataset structure

Primary structured dataset:

```
wifi_data_set/
  id_person_01/
    label_00/
      test_01/
        test1__dev1_64_E8_33_57_AA_F4.data
        test1__dev2_64_E8_33_58_9E_28.data
        test1__dev3_64_E8_33_58_9C_CC.data
      ...
    label_01/ ...
    label_02/ ...
    label_03/ ...
  id_person_02/ ...
  id_person_03/ ...
  id_person_04/ ...
```

Notes:

- Each `test_XX/` folder represents one trial.
- Each trial contains 3 `.data` files (one per receiver device).
- The receiver “device id” and device MAC-like identifier are embedded in the filename.

Raw/original layout (also present in this repo):

```
<label><suffix-of-dashes>/
  testN/
    testN__devK_<device_id>.data
```

Here, the number of trailing `-` characters encodes the person/subject. The scripts `restructure_dataset.py` / `restructure_dataset.ps1` convert the raw layout into `wifi_data_set/`.

## Label meanings

`label_00..label_03` are the 4 class labels present in the dataset.

This repository does **not** contain a textual mapping from label IDs to human-readable class names. If you publish this dataset, consider filling in the table below:

| Label directory | Class name/description |
|---|---|
| `label_00` | TODO |
| `label_01` | TODO |
| `label_02` | TODO |
| `label_03` | TODO |

## File format

Each `.data` file is a newline-delimited text log. Each line contains:

1) A timestamp prefix (with timezone offset), then
2) A CSV record that starts with the literal tag `CSI_DATA`, then
3) A final field which is a JSON-like list of integers (the CSI vector), usually quoted.

Example line (wrapped for readability):

```
18.04.2026 22:44:41.143 +03:00 CSI_DATA,
  328252,50:ff:20:e5:a8:f3,-43,11,1,7,0,1,1,1,1,0,0,-90,0,6,0,669572834,0,83,0,128,0,
  "[0,0,-39,16,-37,13,...]"
```

### Parsed schema (standard `CSI_DATA` layout)

After splitting at `" CSI_DATA,"`, the remaining CSV fields are typically:

| Index | Name | Type | Notes |
|---:|---|---|---|
| 0 | `seq` | int | Frame/packet sequence counter (monotonic within a file) |
| 1 | `mac` | str | Transmitter/AP MAC in colon form (observed constant: `50:ff:20:e5:a8:f3`) |
| 2 | `rssi` | int | Received signal strength (dBm), e.g. `-43` |
| 3 | `rate` | int | PHY rate field as logged by the capture tool |
| 4 | `sig_mode` | int | Signal mode (often 0/1 in ESP32 CSI logs) |
| 5 | `mcs` | int | Modulation and Coding Scheme index (0..7 observed) |
| 6 | `bandwidth` | int | Bandwidth flag (0 observed in valid lines) |
| 7 | `smoothing` | int | 0/1 flag |
| 8 | `not_sounding` | int | 0/1 flag |
| 9 | `aggregation` | int | 0/1 flag |
| 10 | `stbc` | int | 0/1 flag |
| 11 | `fec_coding` | int | 0/1 flag (0 observed) |
| 12 | `sgi` | int | Short guard interval flag (0/1 observed) |
| 13 | `noise_floor` | int | Noise floor (dBm), values like `-90..-98` |
| 14 | `ampdu_cnt` | int | AMPDU counter (0 observed in valid lines) |
| 15 | `channel` | int | WiFi channel (6 observed in valid lines) |
| 16 | `secondary_channel` | int | Secondary channel (0 observed) |
| 17 | `local_timestamp` | int | Device local timestamp (may wrap into negative due to 32-bit overflow) |
| 18 | `ant` | int | Antenna index (0 observed) |
| 19 | `sig_len` | int | Signal length (24/83/118 observed) |
| 20 | `rx_state` | int | RX state (0 observed) |
| 21 | `len` | int | CSI vector length (128 observed in valid lines) |
| 22 | `first_word` | int | First word / reserved (0 observed) |
| 23 | `csi` | list[int] | CSI vector as 128 signed integers |

#### CSI vector

- Length: **128 integers**
- Value range (observed): **[-92, 87]**
- Common interpretation: **64 complex values** as interleaved I/Q pairs: `(I0,Q0,I1,Q1,...)`.

## Known issues / data quality

Most lines parse cleanly, but there are a small number of malformed first lines:

- Valid frames (parse to 24 fields + 128-length CSI vector): **479,402 / 480,000** (99.875%)
- Malformed frames: **598 / 480,000** (0.125%)
- Affected files: **598 / 4,800** (12.46%)
- Pattern: **the malformed record is always line 1** of the affected file; the remaining 99 lines are well-formed.

Typical corruption patterns include missing commas/quotes around the CSI vector, truncated first lines, or concatenated `CSI_DATA` tokens.

If you are building a loader, the simplest robust strategy is:

1) Parse every line, and
2) Keep only records where:
   - CSV field count is exactly 24, and
   - the `csi` field parses as a list of length 128.

## Loading example (Python)

```python
import csv
import json
from dataclasses import dataclass
from datetime import datetime

TS_FMT = "%d.%m.%Y %H:%M:%S.%f %z"

@dataclass(frozen=True)
class CSIFrame:
    ts: datetime
    meta: dict
    csi: list[int]  # length 128

def parse_csi_line(line: str) -> CSIFrame | None:
    if " CSI_DATA," not in line:
        return None
    ts_str, rest = line.split(" CSI_DATA,", 1)
    ts = datetime.strptime(ts_str, TS_FMT)

    row = next(csv.reader([rest], delimiter=",", quotechar='"'))
    if len(row) != 24:
        return None

    try:
        csi = json.loads(row[-1])
    except json.JSONDecodeError:
        return None
    if not isinstance(csi, list) or len(csi) != 128:
        return None

    keys = [
        "seq","mac","rssi","rate","sig_mode","mcs","bandwidth","smoothing","not_sounding",
        "aggregation","stbc","fec_coding","sgi","noise_floor","ampdu_cnt","channel",
        "secondary_channel","local_timestamp","ant","sig_len","rx_state","len","first_word",
    ]
    meta = {k: (int(v) if k != "mac" else v) for k, v in zip(keys, row[:-1])}
    return CSIFrame(ts=ts, meta=meta, csi=[int(x) for x in csi])
```

## Suggested evaluation setups

There is no predefined train/validation/test split. Common choices for this type of dataset:

- **Within-subject split:** random trials into train/test per person.
- **Cross-subject split:** hold out `id_person_0X` as test (leave-one-subject-out).
- **Device generalization:** train on `dev1+dev2`, evaluate on `dev3` (or vice versa).

## License

No license file or explicit license statement is included in this repository. If you plan to publish/share the dataset, add a license and update the YAML header at the top of this README.

## How `wifi_data_set/` was produced

The scripts in the repository root reorganize the raw folder structure:

- `restructure_dataset.py` (Python)
- `restructure_dataset.ps1` (PowerShell)

They map source directories named like `0`, `0-`, `0--`, `0---`, `1`, `1-`, ... into:

`wifi_data_set/id_person_XX/label_XX/test_XX/*.data`

## Citation

If you use this dataset in academic work, consider citing it as:

```bibtex
@dataset{wifi_csi_router_reception_2026,
  title        = {WiFi CSI Router Reception Dataset},
  year         = {2026},
  note         = {Version as of 2026-04-18},
}
```