The speed at which millimeter-Wave (mmWave) channel estimation can be carried out is critical for the adoption of mmWave technologies. This is particularly crucial because mmWave transceivers are equipped with large antenna arrays to combat severe path losses, which consequently creates large channel matrices, whose estimation may incur significant overhead. This paper focuses on the mmWave channel estimation problem. Our objective is to reduce the number of measurements required to reliably estimate the channel. Specifically, channel estimation is posed as a "source compression" problem in which measurements mimic an encoded (compressed) version of the channel. Decoding the observed measurements, a task which is traditionally computationally intensive, is performed using a deep-learning-based approach, facilitating a high-performance channel discovery. Our solution not only outperforms state-of-the-art compressed sensing methods, but it also determines the lower bound on the number of measurements required for reliable channel discovery.
翻译:毫米Wave( mmWave) 频道的估算速度对于采用毫米Wave( mmWave) 频道技术至关重要。 这一点特别重要, 因为 毫米Wave 接收器配备了大型天线阵列, 以对抗严重的路径损失, 从而产生大型频道矩阵, 其估计可能会产生巨大的间接费用。 本文侧重于毫米Wave 频道的估算问题。 我们的目标是减少可靠估计频道所需的测量数量。 具体地说, 频道估算是一个“ 源压缩” 问题, 其测量模拟了该频道的编码( 压缩) 版本。 观察到的测量, 传统上是计算密集型的, 正在使用深层学习方法进行, 便利高性能频道的发现 。 我们的解决方案不仅优于最新压缩频道的测量方法, 而且还决定了可靠频道发现所需的测量次数的下限 。