Source Coding Based Millimeter-Wave Channel Estimation With Deep Learning Based Decoding
Source Coding Based Millimeter-Wave Channel Estimation With Deep Learning Based Decoding
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DOI：
10.1109/tcomm.2021.3072999
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发表时间：
2021-07-01
影响因子：
8.3
通讯作者：
Koksal, Can Emre
中科院分区：
文献类型：
--
作者：
Shabara, Yahia;Ekici, Eylem;Koksal, Can Emre
毫米波（mmWave）信道估计的速度对于毫米波技术的采用至关重要。这一点尤其重要，因为毫米波收发器配备了大型天线阵列来对抗严重的路径损耗，从而产生了大型信道矩阵，其估计可能会产生显着的开销。本文主要研究毫米波信道估计问题。我们的目标是减少可靠估计信道所需的测量次数。具体来说，信道估计是一个“源压缩”问题，其中测量模拟信道的编码（压缩）版本。解码观察到的测量值，这是一项传统上计算密集型的任务，使用基于深度学习的方法执行，促进高性能通道发现。我们的解决方案不仅优于最先进的压缩感知方法，而且还确定了可靠通道发现所需的测量次数的下限。
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.