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。

MIMO-GAN：生成 MIMO 信道建模

MIMO-GAN: Generative MIMO Channel Modeling

Statistical channel models are instrumental to design and evaluate wireless
communication systems. In the millimeter wave bands, such models become acutely
challenging; they must capture the delay, directions, and path gains, for each
link and with high resolution. This paper presents a general modeling
methodology based on training generative neural networks from data. The
proposed generative model consists of a two-stage structure that first predicts
the state of each link (line-of-sight, non-line-of-sight, or outage), and
subsequently feeds this state into a conditional variational autoencoder that
generates the path losses, delays, and angles of arrival and departure for all
its propagation paths. Importantly, minimal prior assumptions are made,
enabling the model to capture complex relationships within the data. The
methodology is demonstrated for 28GHz air-to-ground channels in an urban
environment, with training datasets produced by means of ray tracing.

本文提出了一种基于生成神经网络训练的方法来构建并评估毫米波波段的 28GHz 空地信道通信系统的统计信道模型，该方法考虑了每个链路的延迟、方向和路径增益，并在最小化先验假设的情况下通过训练数据自动生成通道损耗、延迟和角度信息。