GAN-based Massive MIMO Channel Model Trained on Measured Data
2024 27th International Workshop on Smart Antennas (WSA)(2024)
摘要
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.
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关键词
channel model,generative adversarial network,massive MIMO
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