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.
Link to the paper: https://ieeexplore.ieee.org/document/10705115
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.
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.