The DOI matches its Crossref record.
Green Small-Cell Networks
Abstract
The exponentially increasing demand for wireless data services requires a massive network densification that is neither economically nor ecologically viable with the current cellular system architectures. A promising solution to this problem is the concept of small-cell networks (SCNs), which is founded by the idea of a very dense deployment of self-organizing, low-cost, low-power, base stations (BSs). Although SCNs have the potential to significantly increase the capacity of cellular networks while reducing their energy consumption, they pose many new challenges to the optimal system design. We show in this article how a large system analysis based on random matrix theory (RMT) can provide tight and tractable approximations of key performance measures of SCNs.
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
Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation
Di Renzo, M., Haas, H., Ghrayeb, A., Sugiura, S., & Hanzo, L. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2287851 · Cited by 1,446
5G Ultra-Dense Cellular Networks
Ge, X., Tu, S., Mao, G., Wang, C.-X., & Han, T. (2016) · IEEE Wireless Communications · doi:10.1109/mwc.2016.7422408 · Cited by 1,081
Energy Efficient Heterogeneous Cellular Networks
Soh, Y. S., Quek, T. Q. S., Kountouris, M., & Shin, H. (2013) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2013.130503 · Cited by 580
Energy Efficiency in Wireless Networks via Fractional Programming Theory
Zappone, A., & Jorswieck, E. (2015) · Foundations and Trends in Communications and Information Theory · doi:10.1561/0100000088 · Cited by 513
Group Sparse Beamforming for Green Cloud-RAN
(2014) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2014.040214.131770 · Cited by 468
Metadata
- Authors
- Hoydis, J., Kobayashi, M., & Debbah, M.
- Venue
- IEEE Vehicular Technology Magazine
- Published
- 2011-03
- Volume
- 6
- Issue
- 1
- Pages
- 37-43
- ISSN
- 1556-6072 (ISSN-L)
- OpenAlex
- W2031007827
- Cited by
- 463
- References
- 0
- FWCI
- 41.22
- 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: Green Small-Cell Networks. https://app.cochat.ai/papers/10.1109/mvt.2010.939904
- The paper
- Hoydis, J., Kobayashi, M., & Debbah, M. (2011). Green Small-Cell Networks. IEEE Vehicular Technology Magazine. https://doi.org/10.1109/mvt.2010.939904
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.
