Open AccessSpectrum measurement modelling and prediction based on wavelets
Author(s) -
Chen Yunfei,
Oh HeeSeok
Publication year - 2016
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2016.0035
Subject(s) - wavelet , computer science , cognitive radio , spectral density , spectrum (functional analysis) , basis (linear algebra) , wavelet transform , algorithm , radio spectrum , artificial intelligence , pattern recognition (psychology) , mathematics , telecommunications , wireless , physics , geometry , quantum mechanics
In this study, a new spectrum measurement modelling method is proposed for several important frequency bands by using the Daubechies wavelets. On the basis of this method, spectrum measurement prediction is also proposed by using regression. Unlike most previous works that model or predict the occupancy rate of the frequency band of interest, this study models and predicts the power measurements directly to remove the dependence of the model on the challenging detection threshold and also to provide more comprehensive descriptions of the licenced user signals for performance improvement in cognitive radios (CRs). Numerical results show that the new spectrum models have acceptable accuracies. They also show that the proposed spectrum measurement prediction method tracks the trend of the true values well. Therefore, these results are very useful in CR designs.