Datasets:

RS2002
/

WiCount

	---
	language:
	- en
	task_categories:
	- time-series-forecasting
	- time-series-classification
	tags:
	- wireless-sensing
	- csi
	- people-counting
	- wifi
	---

	# WiCount

	## Dataset Description

	- Repository (WiCount subdirectory): [CSI-BERT2/WiCount at main · RS2002/CSI-BERT2](https://github.com/RS2002/CSI-BERT2/tree/main/WiCount)
	- Code: [https://github.com/RS2002/CSI-BERT2](https://github.com/RS2002/CSI-BERT2)
	- Paper: [CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing](https://arxiv.org/abs/2412.06861), IEEE Transactions on Mobile Computing (TMC), 2025
	- Contact: [zzhaock@connect.ust.hk](mailto:zzhaock@connect.ust.hk)
	- Collectors: Zijian Zhao, Tingwei Chen
	- Organization: AI-RAN Lab (hosted by Prof. Guangxu Zhu) in SRIBD, CUHK(SZ)
	- Dataset Summary:
	The WiCount dataset contains synchronized Channel State Information (CSI), Received Signal Strength Indicator (RSSI), and timestamp data collected using ESP32-S3 devices for WiFi-based people number estimation in a meeting room scenario. The dataset includes samples for estimating the number of people (0–3) in the environment.
	- Tasks: People Number Estimation

	## Sample Usage

	To use this dataset with the `CSI-BERT2` code, first clone the repository:

	```bash
	git clone https://github.com/RS2002/CSI-BERT2
	cd CSI-BERT2
	```

	Then you can use the provided scripts for pre-training, fine-tuning, and inference. Replace `<data path>` with the path to the WiCount dataset downloaded from Hugging Face.

	### Pre-training

	```bash
	python pretrain.py --GAN --data_path <data path>
	```
	If you do not want to use the discriminator, you can omit the `--GAN` flag.

	### Fine-tuning for CSI Prediction

	```bash
	python prediction.py --GAN --data_path <data path> --parameters <fold path of the whole pre-trained models>
	```

	### Fine-tuning for CSI Sensing Task (e.g., People Number Estimation)

	For the WiCount dataset, use `task "people"`.

	```bash
	python finetune.py --data_path <data path> --class_num <class num> --task "people" --path <parameter path of the backbone> --mode <mode>
	```
	The `mode` parameter can be set as `0`, `1`, or `2`, corresponding to three experiments in the paper:
	- `0`: Training Set (100Hz), Testing Set (100Hz)
	- `1`: Training Set (100Hz+50Hz), Testing Set (100Hz+50Hz)
	- `2`: Training Set (100Hz), Testing Set (50Hz)

	### Inference for CSI Prediction

	```bash
	python prediction.py --data_path <data path> --parameters <fold path of the whole pretrained models> --eval_percent <the percentage of CSI sequence to be predicted>
	```

	## Dataset Structure

	### Data Instances

	Each instance is a `.csv` file representing a 60-second sample with the following columns:

	- seq: Row number of the entry.
	- timestamp: UTC+8 time of data collection.
	- local_timestamp: ESP32 local time.
	- rssi: Received Signal Strength Indicator.
	- data: CSI data with 104 numbers representing 52 subcarriers, where each subcarrier's complex CSI value is computed as `a[2i] + a[2i+1]j`.
	- Other columns: Additional ESP32 device information (e.g., MAC, MCS details).

	### Data Fields

	\| Field Name \| Description \|
	\| --------------- \| ------------------------------------------------------------ \|
	\| seq \| Row number of the entry \|
	\| timestamp \| UTC+8 time of data collection \|
	\| local_timestamp \| ESP32 local time \|
	\| rssi \| Received Signal Strength Indicator \|
	\| data \| CSI data (104 numbers, representing 52 subcarriers as complex values) \|
	\| Other columns \| Additional ESP32 metadata (e.g., MAC address, MCS details) \|\

	### Data Splits

	The dataset is organized by the number of people (0–3), with each folder containing `.csv` files corresponding to the number of people present in the environment:

	- Folders: 0, 1, 2, 3 (representing the number of people).

	## Dataset Creation

	### Curation Rationale

	The dataset was created to facilitate research on WiFi-based people number estimation using low-cost ESP32-S3 devices, enabling applications in smart environments, occupancy monitoring, and crowd management.

	### Source Data

	- Initial Data Collection:

	Data was collected in an indoor meeting room with a single transmitter and multiple receivers using ESP32-S3 devices. The setup included:

	- Frequency Band: 2.4 GHz
	- Bandwidth: 20 MHz (52 subcarriers)
	- Protocol: 802.11n
	- Waveform: OFDM
	- Sampling Rate: ~100 Hz
	- Antenna Configuration: 1 antenna per device
	- Environment: Indoor with walls and a soft pad to prevent volunteer injuries.

	- Who are the source data producers?
	The data was collected by researchers, with volunteers present in a controlled meeting room environment.

	### Annotations

	- Annotation Process:
	Each `.csv` file is stored in a folder labeled with the number of people present (0–3). No additional manual annotations were provided.
	- Who are the annotators?
	The dataset creators labeled the data based on the experimental setup.

	### Personal and Sensitive Information

	The dataset does not contain personally identifiable information, as it focuses on the number of people (0–3) without associating specific identities or biometric data beyond CSI patterns.

	## Citation

	```bibtex
	@ARTICLE{11278110,
	author={Zhao, Zijian and Meng, Fanyi and Lyu, Zhonghao and Li, Hang and Li, Xiaoyang and Zhu, Guangxu},
	journal={IEEE Transactions on Mobile Computing},
	title={CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing},
	year={2025},
	volume={},
	number={},
	pages={1-17},
	keywords={Wireless communication;Sensors;Wireless sensor networks;Predictive models;Wireless fidelity;Training;Adaptation models;Packet loss;Data models;OFDM;Channel statement information (CSI);CSI prediction;CSI classification;wireless communication;wireless sensing},
	doi={10.1109/TMC.2025.3640420}}
	```