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.

该研究提出了使用生成式渠道建模从信道输入输出测量中学习统计信道模型来代表现场数据，使用 MIMO - GAN 模型将无线信道隐式建模成时域带限脉冲响应分布，评估结果表明在捕获電力、时差和空间相关性统计方面具有高一致性，尤其是平均时延误差小于 3.57ns，功率损失在 - 18.7db 以内。