GAN-based Massive MIMO Channel Model Trained on Measured Data
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917608334295040 |
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| author | Euchner, Florian Sanzi, Janina Henninger, Marcus Brink, Stephan ten |
| author_facet | Euchner, Florian Sanzi, Janina Henninger, Marcus Brink, Stephan ten |
| contents | Wireless channel models are a commonly used tool for the development of wireless telecommunication systems and standards. The currently prevailing geometry-based stochastic channel models (GSCMs) were manually specified for certain environments in a manual process requiring extensive domain knowledge, on the basis of channel measurement campaigns. By taking into account the stochastic distribution of certain channel properties like Rician k-factor, path loss or delay spread, they model the distribution of channel realizations. Instead of this manual process, a generative machine learning model like a generative adversarial network (GAN) may be used to automatically learn the distribution of channel statistics. Subsequently, the GAN's generator may be viewed as a channel model that can replace conventional stochastic or raytracer-based models. We propose a GAN architecture for a massive MIMO channel model, and train it on measurement data produced by a distributed massive MIMO channel sounder. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05321 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GAN-based Massive MIMO Channel Model Trained on Measured Data Euchner, Florian Sanzi, Janina Henninger, Marcus Brink, Stephan ten Information Theory Signal Processing Wireless channel models are a commonly used tool for the development of wireless telecommunication systems and standards. The currently prevailing geometry-based stochastic channel models (GSCMs) were manually specified for certain environments in a manual process requiring extensive domain knowledge, on the basis of channel measurement campaigns. By taking into account the stochastic distribution of certain channel properties like Rician k-factor, path loss or delay spread, they model the distribution of channel realizations. Instead of this manual process, a generative machine learning model like a generative adversarial network (GAN) may be used to automatically learn the distribution of channel statistics. Subsequently, the GAN's generator may be viewed as a channel model that can replace conventional stochastic or raytracer-based models. We propose a GAN architecture for a massive MIMO channel model, and train it on measurement data produced by a distributed massive MIMO channel sounder. |
| title | GAN-based Massive MIMO Channel Model Trained on Measured Data |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2403.05321 |