Abstract

Together with increasing technological developments in recent years, there has been a considerable increase in the size of data transmitted in wireless communication. As a consequence of the increased data size, it has become necessary to use the spectrum more efficiently [1]. Cognitive Radio (CR) has become a subject that has been studied over the last years as a solution to the intense use of spectrum and the accompanying inadequacy of spectrum. CR is a new generation radio type that can identify the spectrum holes and use these holes as opportunistical. CR users intend to use the remaining spectrum from licensed users by continuously scanning the entire spectrum or a specific section of the spectrum [2-6].

Highlights

  • Together with increasing technological developments in recent years, there has been a considerable increase in the size of data transmitted in wireless communication

  • Since the probability distribution function of the largest eigenvalue here is known, if μμnn,pp,0 and σc coefficients are added to both sides of the equation, one side of the equation will converge to Tracy-Widom distribution of order 1 with (n,p→∞) condition (see Eq (2))

  • We have considered the performance problems resulting from the fact that the function that expresses the largest eigenvalue distribution in the covariance / eigenvalue spectrum detection methods up to now cannot express this distribution correctly

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Summary

INTRODUCTION

Together with increasing technological developments in recent years, there has been a considerable increase in the size of data transmitted in wireless communication. CR users intend to use the remaining spectrum from licensed users by continuously scanning the entire spectrum or a specific section of the spectrum [2,3,4,5,6] Different methods such as matched filter [7, 8], energy detection [9, 10], cyclostationary detection [1114], eigenvalue detection [15,16,17], and covariance-based detection [18] are proposed for spectrum sensing in the literature. Since the relevant channel is used opportunistically in the spectrum sensing, it is very important to be able to detect the presence of the signal at the least number of samples or as soon as possible For this reason, variance deviation for matrices of different sizes is shown graphically in Fig. 2 to see the success of the proposed method over the asymptotic approach. When the graph is analysed, it is seen that the corrected variance has a much smaller margin of error than the Lj statistics, especially in small sample numbers

SYSTEM MODEL
DERIVATION OF
Derivation of Pd for MME
Derivation of Pfa and Threshold for the MET
Derivation of Pfa and threshold for the ME-GM
Derivation of Pd for the ME-GM
SIMULATION
Findings
DISCUSSION
CONCLUSION
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