The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Millimeter-wave massive MIMO: the next wireless revolution?
Abstract
The combination of millimeter-wave communications, arrays with a massive number of antennas, and small cell geometries is a symbiotic convergence of technologies that has the potential to dramatically improve wireless access and throughput. This article outlines the benefits, challenges, and potential solutions associated with cellular networks that incorporate these technologies.
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
Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!
Rappaport, T. S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., Wong, G. N., Schulz, J. K., Samimi, M., & Gutierrez, F. (2013) · IEEE Access · doi:10.1109/access.2013.2260813 · Cited by 7,351
Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas
Marzetta, T. L. (2010) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2010.092810.091092 · Cited by 6,302
Five disruptive technology directions for 5G
Boccardi, F., Heath, R. W., Lozano, A., Marzetta, T. L., & Popovski, P. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6736746 · Cited by 3,709
Spatially Sparse Precoding in Millimeter Wave MIMO Systems
Ayach, O. E., Rajagopal, S., Abu-Surra, S., Pi, Z., & Heath, R. W. (2014) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2014.011714.130846 · Cited by 3,557
Energy and spectral efficiency of very large multiuser MIMO systems
Ngo, H. Q., Larsson, E. G., & Marzetta, T. L. (2013) · IEEE Transactions on Communications · doi:10.1109/tcomm.2013.020413.110848 · Cited by 3,173
An Overview of Massive MIMO: Benefits and Challenges
Lu, L., Li, G. Y., Swindlehurst, A. L., Ashikhmin, A., & Zhang, R. (2014) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2014.2317671 · Cited by 2,838
Self-citationRoh, W., Seol, J.-Y., Park, J., Lee, B., Lee, J., Kim, Y., Cho, J., Cheun, K., & Aryanfar, F. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6736750 · Cited by 2,792
An introduction to millimeter-wave mobile broadband systems
Pi, Z., & Khan, F. (2011) · IEEE Communications Magazine · doi:10.1109/mcom.2011.5783993 · Cited by 2,538
Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks
Hur, S., Kim, T., Love, D. J., Krogmeier, J. V., Thomas, T. A., & Ghosh, A. (2013) · IEEE Transactions on Communications · doi:10.1109/tcomm.2013.090513.120848 · Cited by 1,062
Brady, J., Behdad, N., & Sayeed, A. M. (2013) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2013.2254442 · Cited by 731
Cited by
Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems
He, H., Wen, C.-K., Jin, S., & Li, G. Y. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2018.2832128 · Cited by 823
Deep Learning-based Channel Estimation for Beamspace mmWave Massive MIMO Systems
He, H., Wen, C.-K., Jin, S., & Li, G. Y. (2018) · arXiv · doi:10.48550/arxiv.1802.01290 · Cited by 823
A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives
Ahmed, I., Khammari, H., Shahid, A., Musa, A., Kim, K. S., De Poorter, E., & Moerman, I. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2843719 · Cited by 818
Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey
Busari, S. A., Huq, K. M. S., Mumtaz, S., Dai, L., & Rodriguez, J. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2787460 · Cited by 730
Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems
Dong, P., Zhang, H., Li, G. Y., Gaspar, I. S., & NaderiAlizadeh, N. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2925975 · Cited by 437
Metadata
- Authors
- Swindlehurst, A. L., Ayanoglu, E., Heydari, P., & Capolino, F.
- Venue
- IEEE Communications Magazine
- Published
- 2014-09
- Volume
- 52
- Issue
- 9
- Pages
- 56-62
- ISSN
- 0163-6804 (ISSN-L)
- OpenAlex
- W2016999686
- Cited by
- 846
- References
- 12
- FWCI
- 37.50
- Type
- journal article
- Language
- en
- Open access
- closed
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: Millimeter-wave massive MIMO: the next wireless revolution? https://app.cochat.ai/papers/10.1109/mcom.2014.6894453
- The paper
- Swindlehurst, A. L., Ayanoglu, E., Heydari, P., & Capolino, F. (2014). Millimeter-wave massive MIMO: the next wireless revolution? IEEE Communications Magazine. https://doi.org/10.1109/mcom.2014.6894453
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.