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Cited by 561

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Matching theory for future wireless networks: fundamentals and applications

Open access
Gu, Yunan, Saad, Walid, Bennis, Mehdi, Debbah, Merouane, Han, Zhu·2015·IEEE Communications Magazine

Abstract

The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this article, the first comprehensive tutorial on the use of matching theory, a Nobel Prize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then the key solution concepts and algorithmic implementations of this framework are exposed. The developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed.

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Metadata

Authors
Gu, Y., Saad, W., Bennis, M., Debbah, M., & Han, Z.
Venue
IEEE Communications Magazine
Published
2015-05
Volume
53
Issue
5
Pages
52-59
ISSN
0163-6804 (ISSN-L)
OpenAlex
W2099388379
Cited by
561
References
11
FWCI
30.79
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Matching theory for future wireless networks: fundamentals and applications. https://app.cochat.ai/papers/10.1109/mcom.2015.7105641
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Gu, Y., Saad, W., Bennis, M., Debbah, M., & Han, Z. (2015). Matching theory for future wireless networks: fundamentals and applications. IEEE Communications Magazine. https://doi.org/10.1109/mcom.2015.7105641

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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.