PhDthesisfinal

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Application of Computational Intelligence in Cognitive Radio Network for
Efficient Spectrum Utilization, and Speech Therapy
Thesis · March 2017
DOI: 10.13140/RG.2.2.13119.61608
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Spectrum Hole Detection using Computational Intelligence View project
Sunday Iliya
De Montfort University
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Faculty of Technology
Centre for Computational Intelligence, School of Computer
Science and Informatics
Application of Computational Intelligence in
Cognitive Radio Network for Efficient Spectrum
Utilization, and Speech Therapy
PhD THESIS
Sunday Iliya
Submitted in partial fulfilment of the requirements
for the degree of Doctor of Philosophy
August, 2016

I dedicate this thesis to God the Father, the Son and the Holy Spirit, and to my wife
Rose Sunday Iliya
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Abstract
With spectrum becoming an ever scarcer resource, it is critical that new communica-
tion 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 spec-
trums are grossly underutilized while some spectrum (licensed and unlicensed) are over-
crowded. The problem of spectrum scarcity and underutilization can be minimized by
adopting a new paradigm of wireless communication scheme. Advanced CR network or
Dynamic Adaptive Spectrum Sharing is one of the ways to optimize our wireless com-
munications 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 sup-
port vector machine (SVM) regression models for prediction of real world RF power.
The prediction accuracy and generalization was improved by combining different pre-
diction 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 un-
derutilized bands that can serve as potential candidate bands to be exploited by CRs,
spectrum occupancy survey based on long time RF measurement using energy detec-
tor 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
some 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.
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Declaration
This is to certify that:
(i) the thesis comprises only my original work towards the PhD except where indi-
cated,
(ii) due acknowledgement has been made in the text to all other material used,
(iii) the thesis is less than 40,000 words in length, exclusive of table, maps, bibliogra-
phies, appendices and footnotes.
Sunday Iliya
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