论文标题
具有生成对抗网络的宽带通道估计
Wideband Channel Estimation with A Generative Adversarial Network
论文作者
论文摘要
高载体频率（例如毫米波（MMWave）和Terahertz（THZ））的通信需要在低SNR处进行非常大带宽的通道估计。因此，为每个相干带宽分配正交的飞行员音调会导致飞行员数量过多。我们利用生成的对抗网络（GAN）来准确估计低SNR的飞行员的频率选择性通道。拟议的估计器首先学会通过训练生成网络从真实但未知的通道分布中生成通道样本，然后通过根据当前接收的信号来优化网络的输入向量，将训练有素的网络用作当前通道的估算。我们的结果表明，在SNR为-5 dB时，即使采用了具有一位相变的收发器，我们的设计也达到了与SNR = 20 dB的LS估计器相同的通道估计误差，或者在2.5 dB处达到了LMMSE估计器，均具有完全数字体系结构。此外，基于GAN的估计器将所需的飞行员数量减少了约70％，而不会显着增加估计误差和所需的SNR。我们还表明，即使簇和射线的数量发生巨大变化，生成网络似乎也不需要重新培训。
Communication at high carrier frequencies such as millimeter wave (mmWave) and terahertz (THz) requires channel estimation for very large bandwidths at low SNR. Hence, allocating an orthogonal pilot tone for each coherence bandwidth leads to excessive number of pilots. We leverage generative adversarial networks (GANs) to accurately estimate frequency selective channels with few pilots at low SNR. The proposed estimator first learns to produce channel samples from the true but unknown channel distribution via training the generative network, and then uses this trained network as a prior to estimate the current channel by optimizing the network's input vector in light of the current received signal. Our results show that at an SNR of -5 dB, even if a transceiver with one-bit phase shifters is employed, our design achieves the same channel estimation error as an LS estimator with SNR = 20 dB or the LMMSE estimator at 2.5 dB, both with fully digital architectures. Additionally, the GAN-based estimator reduces the required number of pilots by about 70% without significantly increasing the estimation error and required SNR. We also show that the generative network does not appear to require retraining even if the number of clusters and rays change considerably.
