Application of Computational Intelligence in Cognitive Radio Network for Efficient Spectrum Utilization, and Speech Therapy

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dc.contributor.author Iliya, Sunday
dc.date.accessioned 2017-05-15T08:45:10Z
dc.date.available 2017-05-15T08:45:10Z
dc.date.issued 2016-08
dc.identifier.uri http://hdl.handle.net/2086/14171
dc.description.abstract communication systems utilize all the available frequency bands as efficiently as possible in time, frequency and spatial domains. Society requires more high capacity and broadband wireless connectivity, demanding greater access to spectrum. Most of the licensed spectrums are grossly underutilized while some spectrum (licensed and unlicensed) are overcrowded. The problem of spectrum scarcity and underutilization can be minimized by adopting a new paradigm of wireless communication scheme. Advanced Cognitive Radio (CR) network or Dynamic Adaptive Spectrum Sharing is one of the ways to optimize our wireless communications technologies for high data rates while maintaining users’ desired quality of service (QoS) requirements. Scanning a wideband spectrum to find spectrum holes to deliver to users an acceptable quality of service using algorithmic methods requires a lot of time and energy. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the available spectrum holes, and the expected RF power in the channels. This will enable the CR to predictively avoid noisy channels among the idle channels, thus delivering optimum QoS at less radio resources. In this study, spectrum holes search using artificial neural network (ANN) and traditional search methods were simulated. The RF power traffic of some selected channels ranging from 50MHz to 2.5GHz were modelled using optimized ANN and support vector machine (SVM) regression models for prediction of real world RF power. The prediction accuracy and generalization was improved by combining different prediction models with a weighted output to form one model. The meta-parameters of the prediction models were evolved using population based differential evolution and swarm intelligence optimization algorithms. The success of CR network is largely dependent on the overall world knowledge of spectrum utilization in both time, frequency and spatial domains. To identify underutilized bands that can serve as potential candidate bands to be exploited by CRs, spectrum occupancy survey based on long time RF measurement using energy detector was conducted. Results show that the average spectrum utilization of the bands considered within the studied location is less than 30%. Though this research is focused on the application of CI with CR as the main target, the skills and knowledge acquired from the PhD research in CI was applied in ome neighbourhood areas related to the medical field. This includes the use of ANN and SVM for impaired speech segmentation which is the first phase of a research project that aims at developing an artificial speech therapist for speech impaired patients. en
dc.description.sponsorship Petroleum Technology Development Fund (PTDF) Scholarship Board, Nigeria en
dc.language.iso en en
dc.publisher De Montfort University en
dc.title Application of Computational Intelligence in Cognitive Radio Network for Efficient Spectrum Utilization, and Speech Therapy en
dc.type Thesis or dissertation en
dc.publisher.department Faculty of Technology en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en


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