CSI-BERT2

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Model Details

Model Name: CSI-BERT2
Model Type: BERT-inspired transformer for CSI prediction and classification
Version: 2.0
Release Date: August 2025
Developers: Zijian Zhao
Organization: SRIBD, SYSU
License: Apache License 2.0
Paper: CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing

Citation:

@article{zhao2024mining,
  title={CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing},
  author={Zhao, Zijian and Meng, Fanyi and Lyu, Zhonghao and Li, Hang and Li, Xiaoyang and Zhu, Guangxu},
  journal={arXiv preprint arXiv:2412.06861},
  year={2024}
}

Contact: [email protected]
Repository: https://github.com/RS2002/CSI-BERT2
Previous Version: CSI-BERT

Model Description

CSI-BERT2 is an upgraded BERT-inspired transformer model for Channel State Information (CSI) prediction and classification in wireless communication and sensing. It improves upon CSI-BERT with optimized model and code structure, supporting tasks like CSI recovery, prediction, gesture recognition, fall detection, people identification, and people number estimation. The model processes CSI amplitude data and supports adversarial training with a GAN-based discriminator.

Architecture: BERT-based transformer with optional GAN discriminator
Input Format: CSI amplitude (batch_size, length, receiver_num * carrier_dim), attention mask (batch_size, length), optional timestamp (batch_size, length)
Output Format: Hidden states of dimension [batch_size, length, hidden_dim]
Hidden Size: 128
Training Objective: MLM pre-training with GAN (optional) and task-specific fine-tuning
Tasks Supported: CSI recovery, CSI prediction, CSI classification

Training Data

The model was trained on the following datasets:

Public Datasets:
- WiGesture: Gesture recognition, people identification
- WiFall: Action recognition, fall detection, people identification
Proposed Dataset:
- [WiCount](CSI-BERT2/WiCount at main · RS2002/CSI-BERT2): People number estimation
Data Structure:
- Amplitude: (batch_size, length, receiver_num * carrier_dim)
- Timestamp: (batch_size, length) (optional)
- Label: (batch_size)
Note: Refer to CSI-BERT for data preparation details. Custom dataloaders may be needed for specific tasks.

Usage

Installation

git clone https://huggingface.co/RS2002/CSI-BERT2

Example Code

import torch
from model import CSI_BERT2
model = CSI_BERT2.from_pretrained("RS2002/CSI-BERT2")
csi = torch.rand((2, 100, 52))
time_stamp = torch.rand((2, 100))
attention_mask = torch.zeros((2, 100))
y = model(csi,time_stamp,attention_mask)
print(y.shape) # dim: [2,100,128] (batch_size,length,hidden_dim)