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dc.contributor.authorIliya, Sundayen
dc.contributor.authorGoodyer, E. N.en
dc.contributor.authorGongora, Mario Augustoen
dc.contributor.authorGow, J. A.en
dc.date.accessioned2015-09-01T15:08:03Z
dc.date.available2015-09-01T15:08:03Z
dc.date.issued2015-08
dc.identifier.citationIliya, S. et al. (2015) Spectrum Hole Prediction And White Space Ranking For Cognitive Radio Network Using An Artificial Neural Network. International Journal of Scientific and Technology Research, 4 (8)en
dc.identifier.issn2277-8616
dc.identifier.urihttp://hdl.handle.net/2086/11175
dc.identifier.urihttp://www.ijstr.org/final-print/aug2015/Spectrum-Hole-Prediction-And-White-Space-Ranking-For-Cognitive-Radio-Network-Using-An-Artificial-Neural-Network.pdf
dc.description.abstractWith spectrum becoming an ever scarcer resource, it is critical that new communication systems utilize all the available frequency bands as efficiently as possible in time, frequency and spatial domain. rHowever, spectrum allocation policies most of the licensed spectrums grossly underutilized while the unlicensed spectrums are overcrowded. Hence, all future wireless communication devices beequipped with cognitive capability to maximize quality of service (QoS); require a lot of time and energartificial intelligence and machine learning in cognitive radio deliver optimum performance. In this paper, we proposed a novel way of spectrum holes prediction using artificial neural network (ANN). The ANN was trained to adapt to the radio spectrum traffic of 20 channels and the trained network was used for prediction of future spectrum holes. The input of the neural network consist of a time domain vector of length six i.e. minute, hour, date, day, week and month. The output is a vector of length 20 each representing the probability of the channel being idle. The channels are ranked in order of decreasing probability of being idleminimizing We assumed that all the channels have the same noise and quality of service; and only one vacant channel is needed for communication. The result of the spectrum holes search using ANN was compared with that of blind linear and blind stochastic search and was found to be superior. The performance of the ANN that was trained to predict the probability of the channels being idle outperformed the ANN that will predict the exact channel states (busy or idle). In the ANN that was trained to predict the exact channels states, all channels predicted to be idle are randomly searched until the first spectrum hole was found; no information about search direction regarding which channel should be sensed first.en
dc.language.isoenen
dc.publisherInternational Journal of Scientific and Technology Researchen
dc.subjectCognitive Radioen
dc.subjectNeural Networken
dc.subjectPrimary Useren
dc.subjectWhite Spaceen
dc.subjectcomputational intelligenceen
dc.titleSpectrum Hole Prediction And White Space Ranking For Cognitive Radio Network Using An Artificial Neural Networken
dc.typeArticleen
dc.researchgroupDIGITSen
dc.peerreviewedYesen
dc.funderPetroleum Technology Development Fund (PTDF) Scholarship, Nigeria.en
dc.projectidCOGNITIVE RADIOen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.researchinstituteInstitute of Engineering Sciences (IES)en


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