CSI-CLIP ResNet-50
CSI-CLIP is a channel foundation model that aligns channel state information (CSI/CFR) and channel impulse response (CIR) representations through contrastive pre-training. This repository contains the official model-only ResNet-50 weights associated with A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency.
- Code: GREAT-ISAC/CSI-CLIP
- Reference data generation: GREAT-ISAC/Channel-Simulation-Data
- Paper: arXiv:2502.11965
- IEEE: ICMLCN 2025
Weight files
Both files contain the same pre-trained model parameters. They do not contain an optimizer, scheduler, training data, or a downstream task head.
| File | Type | Intended use |
|---|---|---|
model.safetensors | Model-only pre-trained weights | Recommended for safe standalone loading and feature extraction |
model.pth | Model-only PyTorch checkpoint with a model key | Compatibility with the existing fine-tuning scripts |
These are channel-foundation-model pre-training weights, not task-specific checkpoints for positioning, beam management, or LOS/NLOS classification. A downstream head must be trained before task-level inference.
The original checkpoint used the legacy names cfr_backbone, cir_backbone,
proj_cfr, and proj_cir. They were mapped to the names in the public
CSICLIP class without changing the tensors. The public class additionally
expects logit_scale; because the original checkpoint predates that parameter,
it is initialized to the documented CLIP default of log(1 / 0.07). This does
not affect CSI encoder feature extraction. Exact SHA-256 values are recorded in
manifest.json.
Input contract
| Property | Value |
|---|---|
| Input shape | [batch, 2, 256, 256] |
| Channel order | Real, imaginary |
| Dtype | float32 |
| Normalization | Per-sample, per-channel min-max normalization to [0, 1] |
| Normalization epsilon | 1e-8 |
| CIR construction | IFFT of the normalized complex CSI along the last axis |
| CSI embedding size | 256 |
The same preprocessing must be used during training, fine-tuning, and
inference. The repository implementation is authoritative; see
build_cir_cfr_pair in augmentations.py.
Usage
Install the code and its minimal dependencies:
git clone https://github.com/GREAT-ISAC/CSI-CLIP.git
cd CSI-CLIP
pip install -r requirements.txt
After downloading model.safetensors, run the model-loading smoke test:
python load_pretrained.py \
--checkpoint /path/to/model.safetensors
Expected output:
CSI embedding shape: (1, 256)
Run feature extraction on a complex cfr.npy sample:
python load_pretrained.py \
--checkpoint /path/to/model.safetensors \
--input /path/to/scenario/cfr.npy \
--sample-index 0
Training data
The model was pre-trained on simulated DeepMIMO CSI covering multiple wireless scenarios. Generated training arrays are not included in this model repository. A reproducible, model-compatible reference data-generation pipeline is available in Channel Simulation Data. Its committed O1_60 configuration is one runnable reference example; it does not reconstruct the complete multi-scenario checkpoint training data.
Intended use
- Research on wireless/channel foundation models.
- CSI representation and feature extraction.
- Initialization for positioning, beam management, and LOS/NLOS classifiers.
- Non-commercial evaluation and reproducibility studies.
Limitations and out-of-scope use
- The model was trained on simulated data; performance on measured channels is not guaranteed.
- Inputs with different antenna/subcarrier layouts require an explicitly validated adaptation rather than an assumed reshape.
- The released weights do not provide task predictions without a trained downstream head.
- The model is not intended for safety-critical deployment or commercial use.
- Results depend on reproducing the documented preprocessing exactly.
License
The original CSI-CLIP code, these model weights, and the repository-owned data-generation scripts are released under the Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0). Attribution is required and commercial use is not permitted without prior written authorization from the copyright holders. Third-party software, simulators, datasets, and scenario assets remain subject to their respective licenses.
Citation
@inproceedings{jiang2025csi_clip,
title={A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency},
author={Jiang, Jun and Yu, Wenjun and Li, Yunfan and Gao, Yuan and Xu, Shugong},
booktitle={2025 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN)},
pages={1--6},
year={2025},
doi={10.1109/ICMLCN64995.2025.11140262}
}
Contact
For questions, contact Jun Jiang at Jun.Jiang25@student.xjtlu.edu.cn.