Diffusion_channel_est

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Diffusion_channel_est

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Source code of the Paper "Diffusion-Based Generative Prior for Low-Complexity MIMO Channel Estimation"

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# Diffusion-Based Channel Estimation

Source code of the paper 
>B. Fesl, M. Baur, F. Strasser, M. Joham, and W. Utschick,
>"Diffusion-Based Generative Prior for Low-Complexity MIMO Channel Estimation," in IEEE Wireless Communications Letters, 2024.

[[IEEE](https://ieeexplore.ieee.org/document/10705115)] [[arXiv](https://arxiv.org/abs/2403.03545)]

---

## Abstract

This letter proposes a novel channel estimator based on diffusion models (DMs), one of the currently top-rated generative models, with provable convergence to the mean square error (MSE)-optimal estimator. A lightweight convolutional neural network (CNN) with positional embedding of the signal-to-noise ratio (SNR) information is designed to learn the channel distribution in the sparse angular domain. Combined with an estimation strategy that avoids stochastic resampling and truncates reverse diffusion steps that account for lower SNR than the given pilot observation, the resulting DM estimator unifies low complexity and memory overhead. Numerical results exhibit better performance than state-of-the-art estimators.

---

## DMSE Scheduler Package

A standalone implementation of the DMSE scheduler is available as a separate package:

```bash
pip install diffusers-dmse
```

[[PyPI](https://pypi.org/project/diffusers-dmse/)] [[GitHub](https://github.com/benediktfesl/diffusers-dmse)]

This repository contains an application-specific implementation. The standalone package provides a reusable and diffusers-compatible version of the scheduler.

---

## Requirements

The code is tested with `Python 3.10` and `Pytorch 2.1.1`. For further details, see `environment.yml`.

---

## Instructions

1. Download the channel dataset from Zenodo:  
   [https://zenodo.org/records/20737830](https://zenodo.org/records/20737830)

2. Extract the downloaded archive and move the extracted folder `bin` into the repository root.

3. To evaluate the pre-trained models used for the plots in the paper, run:

```
python load_and_eval_dm.py -d cuda:0
```

4. To train a DM from scratch and evaluate the performance afterward, run:

```
python diff_cnn.py -d cuda:0
```

5. To evaluate the baseline estimators, run:

```
python baselines.py
```

## Related repositories

- https://github.com/benediktfesl/Diffusion_MSE
- https://github.com/benediktfesl/diffusers-dmse

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