The DOI matches its Crossref record.
Deep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
Open accessAbstract
This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.
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
Cited by
Zheng, B., You, C., Mei, W., & Zhang, R. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3155305 · Cited by 579
Jensen, T. L., & De Carvalho, E. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9053695 · Cited by 423
Jiang, T., Cheng, H. V., & Yu, W. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3078502 · Cited by 290
Mahmood, M. R., Matin, M. A., Sarigiannidis, P., & Goudos, S. K. (2022) · IEEE Access · doi:10.1109/access.2022.3199689 · Cited by 209
Di Renzo, M., Zappone, A., Debbah, M., Alouini, M.-S., Yuen, C., de Rosny, J., & Tretyakov, S. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3007211 · Cited by 112
Metadata
- Authors
- Elbir, A. M., Papazafeiropoulos, A., Kourtessis, P., & Chatzinotas, S.
- Venue
- IEEE Wireless Communications Letters
- Published
- 2020-09
- Volume
- 9
- Issue
- 9
- Pages
- 1447-1451
- ISSN
- 2162-2337 (ISSN-L)
- 2162-2345
- OpenAlex
- W3003686880
- Cited by
- 242
- References
- 0
- FWCI
- 12.32
- 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 Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems. https://app.cochat.ai/papers/10.1109/lwc.2020.2993699
- The paper
- Elbir, A. M., Papazafeiropoulos, A., Kourtessis, P., & Chatzinotas, S. (2020). Deep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems. IEEE Wireless Communications Letters. https://doi.org/10.1109/lwc.2020.2993699
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.
