Variational Autoencoder for Channel Estimation: Real-World Measurement Insights
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866929336691458048 |
|---|---|
| author | Baur, Michael Böck, Benedikt Turan, Nurettin Utschick, Wolfgang |
| author_facet | Baur, Michael Böck, Benedikt Turan, Nurettin Utschick, Wolfgang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03450 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Variational Autoencoder for Channel Estimation: Real-World Measurement Insights Baur, Michael Böck, Benedikt Turan, Nurettin Utschick, Wolfgang Signal Processing Information Theory 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. |
| title | Variational Autoencoder for Channel Estimation: Real-World Measurement Insights |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2312.03450 |