An Algorithm for Spectrum Hole Detection using Convex Optimization And Tensor Analysis In Cognitive Radio Network
DOI:
https://doi.org/10.14738/jbemi.66.8010Keywords:
Cognitive Radio, Tensor, Spectrum hole, Convex optimization, Covariance Matrix, Eigen Vector, Principal Component AnalysisAbstract
The issue of speed and accuracy is one major challenge in the area of Spectrum hole detection in Cognitive Radio Network(CRN), owing to some of the techniques used in the previous past, noise is sometimes recorded against spectrum hole, and this is mostly due to the method adopted , the need for a more compact procedure as become necessary. An Algorithm for Spectrum Hole Detecting using Convex Optimization and Tensor analysis in Cognitive Radio Network seeks to present a way out of it. The tensor analysis will provide an infinite representation Spectrum data from the wideband, while Convex optimization will help split the large data by grouping it into various spectrum segment, based on the objective function, this grouping will help improve on the speed of Spectrum hole detection. Principal Component Analysis(PCA) checks the level of correction using orthogonal transformation, the use of Eigen Values and Eigen Vectors will further help linearize the function by finding the roots. Covariance matrix will help further check how the variable varies together. It describes the dimension of the spectrum data. Diagonisation is used to extract the matrix with the spectrum data using singular value decomposition; finally, Bayesian inference will optimise decision making for spectrum data.
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