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MIMO Channel Estimation using Score-Based (Diffusion) Generative Models

This repository contains source code for MIMO Channel Estimation using Score-Based Generative Models, and contains code for training and testing a score-based generative model on channels from the Clustered Delay Line (CDL) family of models, as well as other algorithms.

Requirements

Python 3.8, 3.9, or 3.10. Tested on Ubuntu 20.04 and 22.04. MATLAB license required to run MATLAB scripts.

Getting Started

After cloning the repository, run the following commands for Python 3.10 (similar for other versions of Python):

cd score-based-channels
python3.10 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

This will create a self-contained virtual environment in the base directory, activate it, and install all required packages.

Pre-generated Data

Train and validation data for CDL-C channels can be directly downloaded from the command line using the following:

mkdir data
curl -L https://utexas.box.com/shared/static/nmyg5s06r6m2i5u0ykzlhm4vjiqr253m.mat --output ./data/CDL-C_Nt64_Nr16_ULA0.50_seed1234.mat
curl -L https://utexas.box.com/shared/static/2a7tavjo9hk3wyhe9vv0j7s2l6en4mj7.mat --output ./data/CDL-C_Nt64_Nr16_ULA0.50_seed4321.mat

For other channel distributions (CDL-A, CDL-B, CDL-D) shown in the paper the used training and validation data can be downloaded from the following public repository:

https://utexas.box.com/s/f7g7yqdw5w0fea0b59aym3xsvbvw1uch

Once downloaded, place these files in the data folder under the main directory.

Pre-trained Models

A pre-trained diffusion model for CDL-C channels can be directly downloaded from the command line using the following:

mkdir -p models/score/CDL-C
curl -L https://utexas.box.com/shared/static/4nubcpvpuv3gkzfk8dgjo6ay0ssps66w.pt --output ./models/score/CDL-C/final_model.pt

This will create the nested directories models/score/CDL-C and place the weights there. Weights for models trained on other distributions (CDL-A, CDL-B, CDL-D, Mixed) shown in the paper can be downloaded from the following public repository:

https://utexas.box.com/s/m58udx6h0glwxua88zgdwrff87jvy3qw

Once downloaded, places these files in their matching directory structure as final_model.pt.

Training Diffusion Models on MIMO Channels

After downloading the example CDL-C data, a diffusion model can be trained by running:

python train_score.py

The model is trained for 400 epochs by default, and the last model weights will be automatically saved in the model folder under the appropriate structure. To train on other channel distributions, see the --train argument.

Channel Estimation with Diffusion Models

To run channel estimation with the CDL-C data and the pretrained model run:

python test_score.py

This will run channel estimation in the setting of Figure 5c of the paper, and will reproduce the Score-based (CDL-C) curve:

Running the above command will automatically plot and save results in the results/score/train-CDL-C_test-CDL-C folder. To run channel estimation on other channel distributions, see the --train and --test arguments, which dictate what pretrained model should be used and what the test distribution is, respectively.

Hyper-parameter Tuning

Tuning the inference hyper-parameters alpha (the step size in Annealed Langevin Dynamics), beta (a multiplier for the noise added in each step of Annealed Langevin Dynamics), and N (the number of inference steps in Annealed Langevin Dynamics) can be done for the CDL-C pretrained model by running:

python tune_hparams_score.py

This will perform a grid search for the best values of alpha, beta, and N and will save the results in the results/score folder. To modify the searched values and the model that is being tuned, see the alpha_step_range, beta_noise_range, and channel arguments respectively.

Generating Channel Data

For completeness, we also include the Matlab scripts used to generated all training and testing datasets in the matlab folder. The main script to run is matlab/generate_data.m.

Citations

Full credits for the ncsnv2 repository go to: https://github.com/ermongroup/ncsnv2

Please include the following citation when using or referencing this codebase:

@ARTICLE{9957135,
  author={Arvinte, Marius and Tamir, Jonathan I.},
  journal={IEEE Transactions on Wireless Communications}, 
  title={MIMO Channel Estimation Using Score-Based Generative Models}, 
  year={2023},
  volume={22},
  number={6},
  pages={3698-3713},
  doi={10.1109/TWC.2022.3220784}}

Previous related publications are:

@inproceedings{arvinte2022score1,
  title={Score-Based Generative Models for Wireless Channel Modeling and Estimation},
  author={Arvinte, Marius and Tamir, Jonathan},
  booktitle={ICLR Workshop on Deep Generative Models for Highly Structured Data},
  year={2022}
}

@inproceedings{arvinte2022score2,
  title={Score-Based Generative Models for Robust Channel Estimation},
  author={Arvinte, Marius and Tamir, Jonathan I},
  booktitle={2022 IEEE Wireless Communications and Networking Conference (WCNC)},
  pages={453--458},
  year={2022},
  organization={IEEE}
}

	val_Y = np.matmul(val_P, val_H)
	val_Y = val_Y + \
	np.sqrt(local_noise) / np.sqrt(2.) * \
	(np.random.normal(size=val_Y.shape) + \
	1j * np.random.normal(size=val_Y.shape))

	val_Y = torch.matmul(val_P, val_H)
	val_Y = val_Y + \
	np.sqrt(local_noise) * torch.randn_like(val_Y)

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score-based-channels's Introduction

MIMO Channel Estimation using Score-Based (Diffusion) Generative Models

Requirements

Getting Started

Pre-generated Data

Pre-trained Models

Training Diffusion Models on MIMO Channels

Channel Estimation with Diffusion Models

Hyper-parameter Tuning

Generating Channel Data

Citations

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