MIMO-GAN: Generative MIMO Channel Modeling
This provides a tractable and accurate channel modeling approach for wireless communication simulations, though it is incremental as it applies existing GAN techniques to a specific domain.
The paper tackles the problem of modeling stochastic MIMO wireless channels by proposing MIMO-GAN, a generative method that learns channel distributions from measurements, achieving errors of under 3.57 ns average delay and -18.7 dB power on 3GPP TDL MIMO channels.
We propose generative channel modeling to learn statistical channel models from channel input-output measurements. Generative channel models can learn more complicated distributions and represent the field data more faithfully. They are tractable and easy to sample from, which can potentially speed up the simulation rounds. To achieve this, we leverage advances in GAN, which helps us learn an implicit distribution over stochastic MIMO channels from observed measurements. In particular, our approach MIMO-GAN implicitly models the wireless channel as a distribution of time-domain band-limited impulse responses. We evaluate MIMO-GAN on 3GPP TDL MIMO channels and observe high-consistency in capturing power, delay and spatial correlation statistics of the underlying channel. In particular, we observe MIMO-GAN achieve errors of under 3.57 ns average delay and -18.7 dB power.