A Procedure for Developing Spectrum Hole Identification Using Convex Optimization and Tensor Analysis in Cognitive Radio Network

Emeshili O. Joseph, Emmanuel Eronu, Emmanuel Eronu, Evans Ashigwuike, Evans Ashigwuike

Abstract


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 has become necessary. An Algorithm for Spectrum Hole Detection using Convex Optimization and Tensor analysis in Cognitive Radio Network seeks to present a way out of it. The tensor analysis provides an infinite representation of  Spectrum data from the wideband Spectrum, while Convex optimization was used to 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 coherent using linear transformation, the use of Eigen Values and Eigen Vectors was used to stretch out the data. Covariance matrix will help further check how the variable varies with respect to each other using diagonalization.  It describes the dimension of the spectrum data, this method is a model for optimizing spectrum hole detection.


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References


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