GAN-based Massive MIMO Channel Model Trained on Measured Data
<|reference_start|>GAN-based Massive MIMO Channel Model Trained on Measured Data: 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.<|reference_end|>
@article{euchner2024gan-based,
    title={GAN-based Massive MIMO Channel Model Trained on Measured Data},
    author={Florian Euchner, Janina Sanzi, Marcus Henninger, Stephan ten Brink},
    journal={arXiv preprint arXiv:2403.05321},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05321},
    primaryClass={cs.IT eess.SP math.IT}
}