Deep learning for 1-bit compressed sensing-based superimposed CSI feedback

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, 1-bit compressed sensing (CS)-based superimposed channel state information (CSI) feedback has shown many advantages, while still faces many challenges, such as low accuracy of the downlink CSI recovery and l...

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Bibliographic Details
Published in:PloS one 2022-03, Vol.17 (3), p.e0265109-e0265109
Main Authors: Qing, Chaojin, Ye, Qing, Cai, Bin, Liu, Wenhui, Wang, Jiafan
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Bandwidths
Biology and Life Sciences
Communication
Computer and Information Sciences
Deep Learning
Downlinking
Electrical engineering
Engineering and Technology
Feature extraction
Feedback
Frequency division duplexing
Machine learning
Neural networks
Parameter robustness
Physical Sciences
Properties
Recovery
Research and Analysis Methods
Technology
Wireless networks
Wireless telecommunications equipment
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Summary:In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, 1-bit compressed sensing (CS)-based superimposed channel state information (CSI) feedback has shown many advantages, while still faces many challenges, such as low accuracy of the downlink CSI recovery and large processing delays. To overcome these drawbacks, this paper proposes a deep learning (DL) scheme to improve the 1-bit compressed sensing-based superimposed CSI feedback. On the user side, the downlink CSI is compressed with the 1-bit CS technique, superimposed on the uplink user data sequences (UL-US), and then sent back to the base station (BS). At the BS, based on the model-driven approach and assisted by the superimposition-interference cancellation technology, a multi-task detection network is first constructed for detecting both the UL-US and downlink CSI. In particular, this detection network is jointly trained to detect the UL-US and downlink CSI simultaneously, capturing a globally optimized network parameter. Then, with the recovered bits for the downlink CSI, a lightweight reconstruction scheme, which consists of an initial feature extraction of the downlink CSI with the simplified traditional method and a single hidden layer network, is utilized to reconstruct the downlink CSI with low processing delay. Compared with the 1-bit CS-based superimposed CSI feedback scheme, the proposed scheme improves the recovery accuracy of the UL-US and downlink CSI with lower processing delay and possesses robustness against parameter variations.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0265109