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," 2024, arXiv preprint: 2403.03545.
Link to the paper: https://arxiv.org/abs/2403.03545
This work proposes a novel channel estimator based on diffusion models (DMs), one of the currently top-rated generative models. Contrary to related works utilizing generative priors, a lightweight convolutional neural network (CNN) with positional embedding of the signal-to-noise ratio (SNR) information is designed by learning 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 has both low complexity and memory overhead. Numerical results exhibit better performance than state-of-the-art channel estimators utilizing generative priors.
The code is tested with Python 3.10 and Pytorch 2.1.1. For further details, see environment.yml.
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Load channel data from https://syncandshare.lrz.de/getlink/fi93y1AnwmsvHrAGNqq5zX/ (password: Diffusion2024) and move it into folder
bin. -
To evaluate the pre-trained models used for the plots in the paper, run
python load_and_eval_dm.py -d cuda:0
- To train a DM from scatch and evaluate the performance afterward, run
python diff_cnn.py -d cuda:0
- To evaluate the baseline estimators, run
python baselines.py
The code is based on the implementation of https://github.com/benediktfesl/Diffusion_MSE.