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Variational Autoencoder for Channel Estimation: Real-World Measurement Insights
- Publication Year :
- 2023
-
Abstract
- This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and parameterizes an approximation to the mean squared error-optimal estimator by learning observation-dependent conditional first and second moments. The proposed estimator significantly outperforms related state-of-the-art estimators on real-world measurements. We investigate the effect of pre-training with synthetic data and find that the proposed estimator exhibits comparable results to the related estimators if trained on synthetic data and evaluated on the measurement data. Furthermore, pre-training on synthetic data also helps to reduce the required measurement training dataset size.<br />Comment: 6 pages, 6 figures, accepted at WSA 2024
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2312.03450
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/WSA61681.2024.10512030