The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Spectrum Decision in Cognitive Radio Networks: A Survey
Abstract
Spectrum decision is the ability of a cognitive radio (CR) to select the best available spectrum band to satisfy secondary users' (SUs') quality of service (QoS) requirements, without causing harmful interference to licensed or primary users (PUs). Each CR performs spectrum sensing to identify the available spectrum bands and the spectrum decision process selects from these available bands for opportunistic use. Spectrum decision constitutes an important topic which has not been adequately explored in CR research. Spectrum decision involves spectrum characterization, spectrum selection and CR reconfiguration functions. After the available spectrum has been identified, the first step is to characterize it based not only on the current radio environment conditions, but also on the PU activities. The second step involves spectrum selection, whereby the most appropriate spectrum band is selected to satisfy SUs' QoS requirements. Finally, the CR should be able to reconfigure its transmission parameters to allow communication on the selected band. Key to spectrum characterization is PU activity modelling, which is commonly based on historical data to provide the means for predicting future traffic patterns in a given spectrum band. This paper provides an up-to-date survey of spectrum decision in CR networks (CRNs) and addresses issues of spectrum characterization (including PU activity modelling), spectrum selection and CR reconfiguration. For each of these issues, we highlight key open research challenges. We also review practical implementations of spectrum decision in several CR platforms.
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Cited by
Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications
Ali, A., & Hamouda, W. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2631080 · Cited by 624
Khan, A. A., Rehmani, M. H., & Reisslein, M. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2481722 · Cited by 355
Full spectrum sharing in cognitive radio networks toward 5G: a survey
Hu, F., Chen, B., & Zhu, K. (2018) · IEEE Access · doi:10.1109/access.2018.2802450 · Cited by 321
Primary radio user activity models for cognitive radio networks: A survey
Saleem, Y., & Rehmani, M. H. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2014.04.001 · Cited by 278
Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends
Joshi, G., Nam, S., & Kim, S. (2013) · Sensors · doi:10.3390/s130911196 · Cited by 273
Metadata
- Authors
- Masonta, M. T., Mzyece, M., & Ntlatlapa, N.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2013
- Volume
- 15
- Issue
- 3
- Pages
- 1088-1107
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2136957911
- Cited by
- 309
- References
- 38
- FWCI
- 29.12
- Type
- journal article
- Language
- en
- Open access
- closed
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