This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network. Channel estimation using generative networks relies on the assumption that the reconstructed channel lies in the range of a generative model. Channel reconstruction using generative priors outperforms conventional CS techniques and requires fewer pilots. It also eliminates the need of a priori knowledge of the sparsifying basis, instead using the structure captured by the deep generative model as a prior. Using this prior, we also perform channel estimation from one-bit quantized pilot measurements, and propose a novel optimization objective function that attempts to maximize the correlation between the received signal and the generator’s channel estimate while minimizing the rank of the channel estimate. Our approach significantly outperforms sparse signal recovery methods such as Orthogonal Matching Pursuit (OMP) and Approximate Message Passing (AMP) algorithms such as EM-GM-AMP for narrowband mmWave channel reconstruction, and its execution time is not noticeably affected by the increase in the number of received pilot symbols.

中文翻译：

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使用深度生成网络的高维信道估计

本文通过优化深度生成网络的输入，提出了一种用于高维无线信道估计的新型压缩感知 (CS) 方法。使用生成网络的信道估计依赖于重建信道位于生成模型范围内的假设。使用生成先验的信道重建优于传统的 CS 技术并且需要更少的导频。它还消除了对稀疏基础的先验知识的需要，而是使用深度生成模型捕获的结构作为先验。使用这个先验，我们还从一位量化的导频测量中执行​​信道估计，并提出了一种新的优化目标函数，该函数试图最大化接收信号与生成器信道估计之间的相关性，同时最小化信道估计的秩。我们的方法明显优于稀疏信号恢复方法，例如正交匹配追踪 (OMP) 和近似消息传递 (AMP) 算法，例如用于窄带毫米波信道重建的 EM-GM-AMP，并且其执行时间不受接收导频符号的数量。