The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Deep Learning-based Uplink Data Detection in User-Centric Cell-Free mMIMO Systems
Open accessPublished version of a preprint
Abstract
This paper tackles the problem of uplink data detection in a user-centric cell-free massive multi-input multi-output (UC-CF-mMIMO). First of all, we cast the uplink data detection in UC-CF-mMIMO with large-scale fading decoding (LSFD) as a classical MIMO detection problem. Next, we develop a new detection structure, called LMDPIC, which combines linear minimum mean square error (LMMSE) and deep-learning-based parallel interference cancellation (DeepPIC) detectors for symbol detection. Simulation results demonstrate that LMDPIC outperforms other state-of-the-art MIMO detection schemes for BPSK and QPSK modulation schemes, both in the case of the perfect and in that of imperfect channel state information (CSI). We also evaluate the performance of LMDPIC for several power-control strategies. Our results show that for 5% UEs with the highest pairwise symbol error rate, the LMDPIC with half power transmission outperforms the LMMSE with full power transmission. Finally the paper shows that the deep neural network (DNN) used in the LMDPIC structure is robust against CSI time variations.
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
Probability, Random Variables, and Stochastic Processes
Miller, I. (1966) · Technometrics · doi:10.1080/00401706.1966.10490365 · Cited by 8,452
Cell-Free Massive MIMO Versus Small Cells
Ngo, H. Q., Ashikhmin, A., Yang, H., Larsson, E. G., & Marzetta, T. L. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2655515 · Cited by 2,757
Toward 6G Networks: Use Cases and Technologies
Giordani, M., Polese, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900411 · Cited by 2,241
Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation
Bjornson, E., & Sanguinetti, L. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2941478 · Cited by 974
Edge artificial intelligence for 6g: vision, enabling technologies, and applications
Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126076 · Cited by 829
Foundations of User-Centric Cell-Free Massive MIMO
Demir, Ȕ. T., BjɆrnson, E., & Sanguinetti, L. (2021) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000109 · Cited by 704
Massive MIMO Detection Techniques: A Survey
Albreem, M. A., Juntti, M., & Shahabuddin, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2935810 · Cited by 505
The Road to 6G: Ten Physical Layer Challenges for Communications Engineers
Matthaiou, M., Yurduseven, O., Ngo, H. Q., Morales-Jimenez, D., Cotton, S. L., & Fusco, V. F. (2021) · IEEE Communications Magazine · doi:10.1109/mcom.001.2000208 · Cited by 370
Buzzi, S., D'Andrea, C., Zappone, A., & D'Elia, C. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2952117 · Cited by 348
Self-citationPositional Accuracy of Assisted GPS Data from High-Sensitivity GPS-enabled Mobile Phones
Zandbergen, P. A., & Barbeau, S. (2011) · Journal of Navigation · doi:10.1017/s0373463311000051 · Cited by 314
Metadata
- Authors
- Hematidoust, N., Tadaion, A., Buzzi, S., Magarini, M., & Sadeghi, M. T.
- Affiliations
- Yazd University (IR)
- Università degli studi di Cassino e del Lazio Meridionale (IT)
- Politecnico di Milano (IT)
- Corresponding author
- Tadaion, Aliakbar
- Venue
- Iranian Journal of Science and Technology, Transactions of Electrical Engineering
- Published
- 2025-04-29
- Volume
- 49
- Issue
- 3
- Pages
- 983-999
- ISSN
- 2228-6179 (ISSN-L)
- 2364-1827
- OpenAlex
- W4409908874
- Cited by
- 2
- References
- 34
- Citations by year
- 2026: 2
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- Iranian Journal of Science and Technology Transactions of Electrical Engineering (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced MIMO Systems Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Keywords
- large-scale fading decoding, linear minimum mean square error, symbol detection, imperfect channel state information, power control, deep neural network robustness
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-based Uplink Data Detection in User-Centric Cell-Free mMIMO Systems. https://app.cochat.ai/papers/10.1007/s40998-025-00811-8
- The paper
- Hematidoust, N., Tadaion, A., Buzzi, S., Magarini, M., & Sadeghi, M. T. (2025). Deep Learning-based Uplink Data Detection in User-Centric Cell-Free mMIMO Systems. Iranian Journal of Science and Technology, Transactions of Electrical Engineering. https://doi.org/10.1007/s40998-025-00811-8
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.
