Machine Learning for Channel Estimation from Compressed Measurements
Machine Learning for Channel Estimation from Compressed Measurements
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DOI：
10.1109/iswcs.2018.8491199
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
M. Koller;C. Hellings;Michael Knoedlseder;Thomas Wiese;David Neumann;W. Utschick
中科院分区：
文献类型：
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作者：
M. Koller;C. Hellings;Michael Knoedlseder;Thomas Wiese;David Neumann;W. Utschick
最近有人提出使用卷积神经网络来估计结构化信道，例如，接收功率集中在少数传播路径中心周围的信道。在仿真中，该方法对于具有大量天线的系统也显示出良好的性能，但它没有考虑到这样的系统可能具有比接收天线更少的接收链。在这种情况下，模拟混合网络将天线连接到接收器链，这将导致低维观测。在本文中，我们研究了如何使用机器学习方法从这些压缩测量中估计信道。
It has recently been proposed to employ convolutional neural networks for estimating structured channels, e.g., channels where the received power is concentrated around the centers of a small number of propagation paths. In simulations, the approach shows good performance also for systems with a high number of antennas, but it does not consider that such systems might have less receiver chains than receive antennas. In this case, an analog mixing network would connect the antennas to the receiver chains, which results in low-dimensional observations. In this paper, we study how the machine learning approach can be used to estimate the channel from such compressed measurements.