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dc.contributor.authorIliya, Sundayen
dc.contributor.authorGoodyer, E. N.en
dc.contributor.authorShell, Jethroen
dc.contributor.authorGow, J. A.en
dc.contributor.authorGongora, Mario Augustoen
dc.date.accessioned2014-09-30T10:18:15Z
dc.date.available2014-09-30T10:18:15Z
dc.date.issued2014-10
dc.identifier.citationIliya, S. et al. (2014) Optimized Neural Network Using Differential Evolutionary and Swarm Intelligence Optimization Algorithms for RF Power Prediction in Cognitive Radio Network: A Comparative study. 2014 IEEE 6th International Conference On Adaptive Science & Technology (ICAST)en
dc.identifier.urihttp://hdl.handle.net/2086/10298
dc.description.abstractCognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The a priory knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance. This will enable the selection of channel with less noise among idle (free) channels. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. The models used, implemented a novel and innovative initial weight optimization of the ANN’s through the use of differential evolutionary and swarm intelligence algorithms. This was found to enhance the accuracy and generalization of the ANN model. For this problem, DE/best/1/bin was found to yield a better performance as compared with the other algorithms implemented.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectCognitive Radioen
dc.titleOptimized Neural Network Using Differential Evolutionary and Swarm Intelligence Optimization Algorithms for RF Power Prediction in Cognitive Radio Network: A Comparative studyen
dc.typeConferenceen
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.fundernoneen
dc.projectidnoneen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.researchinstituteInstitute of Engineering Sciences (IES)en


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