The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Deep Learning for Massive MIMO CSI Feedback
Open accessAbstract
In frequency division duplex mode, the downlink channel 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Cited by
Deep Learning in Physical Layer Communications
Qin, Z., Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800601 · Cited by 692
Massive Access for 5G and Beyond
Chen, X., Ng, D. W. K., Yu, W., Larsson, E. G., Al-Dhahir, N., & Schober, R. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3019724 · Cited by 681
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Simeone, O. (2018) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2018.2881442 · Cited by 516
Model-Driven Deep Learning for Physical Layer Communications
He, H., Jin, S., Wen, C.-K., Gao, F., Li, G. Y., & Xu, Z. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800447 · Cited by 481
Self-citationModel-Driven Deep Learning for MIMO Detection
He, H., Wen, C.-K., Jin, S., & Li, G. Y. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2976585 · Cited by 454
Self-citation
Metadata
- Authors
- Wen, C.-K., Shih, W.-T., & Jin, S.
- Venue
- IEEE Wireless Communications Letters
- Published
- 2018-10
- Volume
- 7
- Issue
- 5
- Pages
- 748-751
- ISSN
- 2162-2337 (ISSN-L)
- 2162-2345
- OpenAlex
- W2963145597
- Cited by
- 1,174
- References
- 1
- FWCI
- 31.24
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deep Learning for Massive MIMO CSI Feedback. https://app.cochat.ai/papers/10.1109/lwc.2018.2818160
- The paper
- Wen, C.-K., Shih, W.-T., & Jin, S. (2018). Deep Learning for Massive MIMO CSI Feedback. IEEE Wireless Communications Letters. https://doi.org/10.1109/lwc.2018.2818160
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
