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 knowledg...

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Bibliographic Details
Published in:arXiv.org 2024-03
Main Authors: Euchner, Florian, Sanzi, Janina, Henninger, Marcus, Stephan ten Brink
Format: Article
Language:English
Subjects:
Generative adversarial networks
Machine learning
MIMO communication
Online Access:Get full text
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Summary: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.
ISSN:2331-8422