PSO of Neural Networks to Predict Busy Times of Cellular Traffic for Assignment to TV Idle Channels by Cognitive Radio

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dc.contributor.authorOjenge, Winston
dc.contributor.authorAfullo, Thomas
dc.contributor.authorOgao, Patrick
dc.contributor.authorOkello Odongo, William
dc.date.accessioned2022-12-26T11:05:54Z
dc.date.available2022-12-26T11:05:54Z
dc.date.issued2013
dc.description.abstractKenya has identified radio spectrum as a key driver in its development. Yet, globally, radio spectrum is inefficiently utilized due to ITU’s static spectrum allocation. In Kenya, mobile operators are running short of bandwidth due to deployment of 4G services, which enable superfast mobile broadband/internet. In the USA and UK, FCC and Ofcom, respectively, have made effort to allow opportunistic ‘poaching’ of licensed spectrum as long as communication of licensed user is not interfered with. This has focused research on use of cognitive radio, which would use its sensor networks to establish which TV channels are idle in order to allocate them temporarily to cellular networks. Enabling the cognitive radio to predict which channels shall lie idle at what times introduces better planning and more temporally-efficient allocation. This study explores the viability of predicting the times of mobile telephony traffic jam for a mobile service operator with poor QoS rating within a cell of perennial mobile traffic jam in order to explore whether those times can map well with the TV spectrum holes. The times of the TV spectrum holes shall be determined in a later study.en_US
dc.identifier.citationWinston, O., Thomas, A., Patrick, O., & William, O. (2013, November). PSO of neural networks to predict busy times of cellular traffic for assignment to TV idle channels by cognitive radio. In 2013 European Modelling Symposium (pp. 48-52). IEEE. DOI 46 10.1109/EMS.2013.8en_US
dc.identifier.other46 10.1109/EMS.2013.8
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/6559
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectCognitive Radioen_US
dc.subjectMobile Telephony Trafficen_US
dc.subjectNNen_US
dc.subjectPSOen_US
dc.titlePSO of Neural Networks to Predict Busy Times of Cellular Traffic for Assignment to TV Idle Channels by Cognitive Radioen_US
dc.typeArticleen_US
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