The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications
Abstract
Due to the under-utilization problem of the allocated radio spectrum, cognitive radio (CR) communications have recently emerged as a reliable and effective solution. Among various network models, this survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service. Spectrum sensing provides the essential information to enable this interweave communications in which primary and secondary users are not allowed to access the medium concurrently. Several researchers have already considered various aspects to realize efficient techniques for spectrum sensing. In this direction, this survey paper provides a detailed review of the state-of-the-art related to the application of spectrum sensing in CR communications. Starting with the basic principles and the main features of interweave communications, this paper provides a classification of the main approaches based on the radio parameters. Subsequently, we review the existing spectrum sensing works applied to different categories such as narrowband sensing, narrowband spectrum monitoring, wideband sensing, cooperative sensing, practical implementation considerations for various techniques, and the recent standards that rely on the interweave network model. Furthermore, we present the latest advances related to the implementation of the legacy spectrum sensing approaches. Finally, we conclude this survey paper with some suggested open research challenges and future directions for the CR networks in next generation Internet-of-Things applications.
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Cites
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Sensing-Throughput Tradeoff for Cognitive Radio Networks
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Cited by
Sutton, G. J., Zeng, J., Liu, R. P., Ni, W., Nguyen, D. N., Jayawickrama, B. A., Huang, X., Abolhasan, M., Zhang, Z., Dutkiewicz, E., et al. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2897800 · Cited by 272
A Review of Sparse Recovery Algorithms
Crespo Marques, E., Maciel, N., Naviner, L., Cai, H., & Yang, J. (2019) · IEEE Access · doi:10.1109/access.2018.2886471 · Cited by 268
Sharma, S. K., Bogale, T. E., Le, L. B., Chatzinotas, S., Wang, X., & Ottersten, B. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2773628 · Cited by 248
Metadata
- Authors
- Ali, A., & Hamouda, W.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2017
- Volume
- 19
- Issue
- 2
- Pages
- 1277-1304
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2551956255
- Cited by
- 624
- References
- 86
- FWCI
- 59.69
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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