Predicting Radio Resource Availability in Cognitive Radio - an Experimental Examination

Kasahara, Shoji; Nomoto, Satoshi; Ueda, Tohru; Nomura, Shusaku; Takeuchi, Kazunori

doi:10.1109/crowncom.2008.4562504

Cited by 19 publications

(16 citation statements)

References 4 publications

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“…In [63], the radio resource availability of the WLAN channel, defined as the sum of the packet occupation time in the channel in seconds, was predicted using the AR model. The order of the AR model was chosen between 10 and 18, depending on the data considered and the mean squared error requirement.…”

Section: Spectrum Occupancy Predictionmentioning

confidence: 99%

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Chen

2016

IEEE Commun. Surv. Tutorials

209

105

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Section: Spectrum Occupancy Predictionmentioning

confidence: 99%

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Chen

2016

IEEE Commun. Surv. Tutorials

209

105

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“…[21]. 3 Two sets of points A and B are linearly separable if there exists n real numbers w1, w2, w3, .., wn, such that every point ai ∈ A satisfies n i=1 wi.ai > ρ and every point bi ∈ B satisfies n i=1 wi.bi < ρ , where ρ represents the constant separating two sets A and B [20]. February 10, 2016 DRAFT…”

Section: Support Vector Machinesmentioning

confidence: 99%

Analysis of Spectrum Occupancy Using Machine Learning Algorithms

Azmat

Chen

Stocks

2016

IEEE Trans. Veh. Technol.

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Original citation: Azmat, F., Chen, Yunfei and Stocks, N. G.. (2015) Analysis of spectrum occupancy using machine learning algorithms. IEEE Transactions on Vehicular Technology. Permanent WRAP url:http://wrap.warwick.ac.uk/76672 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. evaluate the blocking probability of secondary user for future time slots, which can be used by system designers to define spectrum allocation and spectrum sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, we proposed a new SVM algorithm by combining it with fire fly algorithm (FFA), which is shown to outperform all other algorithms. Index TermsFire fly algorithm, hidden markov model, spectrum occupancy and support vector machine.

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“…The authors in [1] survey the main approaches applied in the literature for channel prediction in CR context. [4] to evaluate the radio resource availability in 802.11 networks scenario and apply multi-step-ahead prediction derived through an auto-regression (AR) Model. They apply their technique to 802.11 network data traffic by measuring the radio resource availability through Network Allocation Vector (NAV).…”

Section: Related Workmentioning

confidence: 99%

Hypernetworks based Radio Spectrum Profiling in Cognitive Radio Networks

Khan¹,

Mitschele‐Thiel²

2015

EAI Endorsed Transactions on Cognitive Communications

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This paper presents a novel concept of active radio spectrum profiling f or C ognitive R adio ( CR) networks using evolutionary hypernetworks. Spectrum profiling enables cognitive radio nodes to abstract and predict usable spectrum opportunities in pre-defined P rimary U sers ( PU) c hannels. T he P U c hannels a re actively monitored through spectrum sensing and the resulting binary time series are used for channel abstraction and prediction. An overlay spectrum sharing approach is assumed in this paper and the evolutionary hypernetworks are used for the realization of the radio spectrum profiling concept. The abstracted information not only facilitates the optimization of channel selection and mobility, but also improves the quality of service for the secondary user applications. This paper presents the main concepts, their application to CR ad hoc networks, and an analysis of its impact on the CR network performance.

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Predicting Radio Resource Availability in Cognitive Radio - an Experimental Examination

Cited by 19 publications

References 4 publications

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Analysis of Spectrum Occupancy Using Machine Learning Algorithms

Hypernetworks based Radio Spectrum Profiling in Cognitive Radio Networks

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