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Deep Learning for Massive MIMO CSI Feedback
By : Rizki Rivai Ginanjar
Date : 2018-09-28
Views : 69

Abstract—In frequency division duplex mode, the downlink chan-
nel state information (CSI) should be sent to the base station through
feedback links so that the potential gains of a massive multiple-
input multiple-output can be exhibited. However, such a transmission
is hindered by excessive feedback overhead. In this letter, we use
deep learning technology to develop CsiNet, a novel CSI sensing and
recovery mechanism that learns to effectively use channel structure
from training samples. CsiNet learns a transformation from CSI
to a near-optimal number of representations (or codewords) and
an inverse transformation from codewords to CSI. We perform
experiments to demonstrate that CsiNet can recover CSI with
significantly improved reconstruction quality compared with existing
compressive sensing (CS)-based methods. Even at excessively low
compression regions where CS-based methods cannot work, CsiNet
retains effective beamforming gain.
Attachment 1:   ÷ 08322184.pdf(414.4KB)  
Attachment 2:   ÷ Seminar Deep Learning for Massive MIMO CSI Feedback.pdf(2.3MB)  
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