论文标题
MIMO-GAN：生成的MIMO通道建模
MIMO-GAN: Generative MIMO Channel Modeling
论文作者
论文摘要
我们建议从通道输入输出测量值学习统计通道模型。生成的渠道模型可以学习更多复杂的分布，并更忠实地表示现场数据。它们是可拖动的，易于从中采样，这可能会加快模拟回合的速度。为了实现这一目标，我们利用GAN的进步，这有助于我们从观察到的测量值中学习对随机MIMO通道的隐式分布。特别是，我们的方法MIMO-GAN隐式地将无线通道模拟为时间域带限制脉冲响应的分布。我们在3GPP TDL MIMO通道上评估了MIMO-GAN，并观察到捕获基础通道的功率，延迟和空间相关统计的高频。特别是，我们观察到MIMO -GAN达到了3.57 ns平均延迟和-18.7 dB功率的错误。
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.
