The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Deep Unfolding for Communications Systems: A Survey and Some New Directions
Open accessAbstract
Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms with tools from neural networks to efficiently solve a range of tasks in machine learning, signal and image processing, and communication systems. This survey summarizes the principle of deep unfolding and discusses its recent use for communication systems with focus on detection and precoding in multi-antenna (MIMO) wireless systems and belief propagation decoding of error-correcting codes. To showcase the efficacy and generality of deep unfolding, we describe a range of other tasks relevant to communication systems that can be solved using this emerging paradigm. We conclude the survey by outlining a list of open research problems and future research directions.
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
Parikh, N., & Boyd, S. (2014) · Foundations and Trends® in Optimization · doi:10.1561/2400000003 · Cited by 4,102
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
Learning Fast Approximations of Sparse Coding
Gregor, K., & LeCun, Y. (2010) · Cited by 1,413
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey
Mao, Q., Hu, F., & Hao, Q. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2846401 · Cited by 789
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
Deep learning for wireless physical layer: Opportunities and challenges
Wang, T., Wen, C.-K., Wang, H., Gao, F., Jiang, T., & Jin, S. (2017) · China Communications · doi:10.1109/cc.2017.8233654 · Cited by 606
Learning to decode linear codes using deep learning
Nachmani, E., Be'ery, Y., & Burshtein, D. (2016) · 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton) · doi:10.1109/allerton.2016.7852251 · Cited by 561
Deep Learning Methods for Improved Decoding of Linear Codes
Nachmani, E., Marciano, E., Lugosch, L., Gross, W. J., Burshtein, D., & Be'ery, Y. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2788405 · Cited by 545
Samuel, N., Diskin, T., & Wiesel, A. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2019.2899805 · Cited by 515
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
Cited by
She, C., Sun, C., Gu, Z., Li, Y., Yang, C., Poor, H. V., & Vucetic, B. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3053601 · Cited by 405
Hu, Q., Cai, Y., Shi, Q., Xu, K., Yu, G., & Ding, Z. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3033334 · Cited by 253
Unfolding WMMSE Using Graph Neural Networks for Efficient Power Allocation
Chowdhury, A., Verma, G., Rao, C., Swami, A., & Segarra, S. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3071480 · Cited by 162
Hu, Q., Liu, Y., Cai, Y., Yu, G., & Ding, Z. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3087233 · Cited by 64
Learning to Continuously Optimize Wireless Resource in Episodically Dynamic Environment
Sun, H., Pu, W., Zhu, M., Fu, X., Chang, T.-H., & Hong, M. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9413503 · Cited by 21
Metadata
- Authors
- Balatsoukas-Stimming, A., & Studer, C.
- Venue
- 2019 IEEE International Workshop on Signal Processing Systems (SiPS)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 266-271
- OpenAlex
- W2950850286
- Cited by
- 266
- References
- 49
- FWCI
- 54.78
- Type
- conference paper
- 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 Unfolding for Communications Systems: A Survey and Some New Directions. https://app.cochat.ai/papers/10.1109/sips47522.2019.9020494
- The paper
- Balatsoukas-Stimming, A., & Studer, C. (2019). Deep Unfolding for Communications Systems: A Survey and Some New Directions. 2019 IEEE International Workshop on Signal Processing Systems (SiPS). https://doi.org/10.1109/sips47522.2019.9020494
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.
