Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm
Date
2015
Authors
Iliya, Sunday
Gongora, Mario Augusto
Goodyer, E. N.
Gow, J. A.
Shell, Jethro
Journal Title
Journal ISSN
ISSN
2300-5963
Volume Title
Publisher
IEEE
Peer reviewed
Yes
Abstract
Cognitive radio (CR) technology has emerged as a
promising solution to many wireless communication problems
including spectrum scarcity and underutilization. To enhance
the selection of channel with less noise among the white spaces
(idle channels), the a priory knowledge of Radio Frequency
(RF) power is very important. Computational Intelligence (CI)
techniques cans 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) and
Support Vector Regression (SVR) models for the prediction of
real world RF power within the GSM 900, Very High Frequency
(VHF) and Ultra High Frequency (UHF) FM and TV bands.
Sensitivity analysis was used to reduce the input vector of the
prediction models. The inputs of the ANN and SVR consist of
only time domain data and past RF power without using any RF
power related parameters, thus forming a nonlinear time series
prediction model. 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. Since CR are embedded communication devices with memory constrain limitation, the models used, implemented a novel and innovative initial weight optimization of the ANN’s through the use of compact differential evolutionary (cDE) algorithm variants which are memory efficient. This was found to enhance the accuracy and generalization of the ANN model
Description
Keywords
Cognitive Radio, Primary User, Artificial Neu- ral Network, Support Vector Machine, Compact Differential Evolution, RF Power, Prediction
Citation
Iliya, S. et al. (2015) Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm. 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 55-66
Research Institute
Institute of Artificial Intelligence (IAI)
Institute of Engineering Sciences (IES)
Institute of Engineering Sciences (IES)