This is the simulation code for the article:
M. Baur, N. Turan, B. Fesl, and W. Utschick, "Channel Estimation in Underdetermined Systems Utilizing Variational Autoencoders," IEEE ICASSP, 2024, doi: 10.1109/ICASSP48485.2024.10447622.
In this work, we propose to utilize a variational autoencoder (VAE) for channel estimation (CE) in underdetermined (UD) systems. The basis of the method forms a recently proposed concept in which a VAE is trained on channel state information (CSI) data and used to parameterize an approximation to the mean squared error (MSE)-optimal estimator. The contributions in this work extend the existing framework from fully-determined (FD) to UD systems, which are of high practical relevance. Particularly noteworthy is the extension of the estimator variant, which does not require perfect CSI during its offline training phase. This is a significant advantage compared to most other deep learning (DL)-based CE methods, where perfect CSI during the training phase is a crucial prerequisite. Numerical simulations for hybrid and wideband systems demonstrate the excellent performance of the proposed methods compared to related estimators.
Please download the data under this link by clicking on the Download button. The password is VAE-est-ud-2024!. Afterward, place the data folder in the same directory as the datasets and models folders.
The scripts for reproducing the paper results are eval_baselines_hybrid.py and eval_baselines_wideband.py. The remaining files contain auxiliary functions and classes. The folder models contains the pre-trained model weights with corresponding config files.
This code is written in Python version 3.8. It uses the deep learning library PyTorch and the numpy, scipy, matplotlib, and json packages. The code was tested with the versions visible in the requirements file.
Alternatively, a conda environment that meets the requirements can be created by executing the following lines:
conda create -n vae_est_ud python=3.8 numpy=1.23.5 matplotlib=3.6.2 scipy=1.10.0 simplejson
conda activate vae_est_ud
conda install pytorch cpuonly -c pytorch
Run eval_baselines_hybrid.py to reproduce the hybrid system results from the paper or eval_baselines_wideband.py to reproduce the wideband system results. To this end, adapt the simulation parameters at the beginning of the file to your needs. Models are only available for the scenarios from the paper. Other scenario parameters will result in an error message.